SourceSpec documentation

Earthquake source parameters from P- or S-wave displacement spectra

Copyright:

2011-2024 Claudio Satriano satriano@ipgp.fr

Release:

1.8

Date:

May 07, 2024

SourceSpec is a collection of command line tools to compute earthquake source parameters (seismic moment, corner frequency, radiated energy, source size, static stress drop, apparent stress) from the inversion of P-wave and S-wave displacement spectra recorded at one or more seismic stations. SourceSpec also computes attenuation parameters (t-star, quality factor) and, as a bonus, local magnitude.

See Madariaga [2011] for a primer on earthquake source parameters and scaling laws.

Go to section Theoretical Background to get more information on how the code works.

SourceSpec is written in Python and requires a working Python environment to run (see Installation). However, since SourceSpec is based on command line, you don’t have to know how to code in Python to use it.

The SourceSpec package is made of several command line tools:

• source_spec: Compute earthquake source parameters from the inversion of P- or S-wave spectra.

• source_model: Direct modelling of P- or S-wave spectra, based on user-defined earthquake source parameters.

• source_residuals: Compute station residuals from source_spec output.

• clipping_detection: Test the clipping detection algorithm.

• plot_sourcepars: 1D or 2D plot of source parameters from a sqlite parameter file.

Contents:

Theoretical Background

Overview

source_spec inverts the P- or S-wave displacement amplitude spectra from station recordings of a single event.

For each station, the code computes P- or S-wave displacement amplitude spectra for each component \(x\) (e.g., Z, N, E), within a predefined time window.

\[S_x^{p|s}(f) = \left| \int_{t^{p|s}_0}^{t^{p|s}_1} s_x(t) e^{-i 2 \pi f t} dt \right|\]

where the exponent \(p|s\) means that we are considering either P- or S-waves, \(t^{p|s}_0\) and \(t^{p|s}_1\) are the start and end times of the P- or S-wave time window, \(s_x(t)\) is the displacement time series for component \(x\), and \(f\) is the frequency.

Note that the Fourier amplitude spectrum of ground displacement has the dimensions of displacement (\(s_x(t)\)) multiplied by time (\(dt\)).

The same thing is done for a noise time window: noise spectrum is used to compute spectral signal-to-noise ratio (and possibly reject low SNR spectra) and, optionally, to weight the spectral inversion.

Example three-component trace plot (in velocity), showing noise and S-wave windows.

The component spectra are combined through the root sum of squares (e.g., Z, N, E):

\[S^{p|s}(f) = \sqrt{ \left( S^{p|s}_z(f) \right)^2 + \left( S^{p|s}_n(f) \right)^2 + \left( S^{p|s}_e(f) \right)^2 }\]

(This is actually done later in the code, after converting the spectra to magnitude units, see below.)

Example displacement spectrum for noise and S-wave, including inversion results.

Spectral model

The Fourier amplitude spectrum of the P- or S-wave displacement in far field can be modelled as the product of a source term [Brune, 1970] and a propagation term (geometric and anelastic attenuation of body waves):

\[S^{p|s}(f) = \frac{1}{\mathcal{G}(r)} \times \frac{F R_{\Theta\Phi}} {4 \pi \rho_h^{1/2} \rho_r^{1/2} c_h^{5/2} c_r^{1/2}} \times M_O \times \frac{1}{1+\left(\frac{f}{f^{p|s}_c}\right)^2} \times e^{- \pi f t^*}\]

where:

• \(\mathcal{G}(r)\) is the geometrical spreading coefficient (see below) and \(r\) is the hypocentral distance;

• \(F\) is the free surface amplification factor (generally assumed to be \(2\));

• \(R_{\Theta\Phi}\) is the radiation pattern coefficient for P- or S-waves (average or computed from focal mechanism, if available);

• \(\rho_h\) and \(\rho_r\) are the medium densities at the hypocenter and at the receiver, respectively;

• \(c_h\) and \(c_r\) are the P- or S-wave velocities at the hypocenter and at the receiver, respectively;

• \(M_O\) is the seismic moment;

• \(f\) is the frequency;

• \(f^{p|s}_c\) is the corner frequency for P- or S-waves;

• \(t^*\) is an attenuation parameter which includes anelastic path attenuation (quality factor) and station-specific effects.

Geometrical spreading

The geometrical spreading coefficient \(\mathcal{G}(r)\) can be defined, for local and regional distances, in one of the following ways (see the geom_spred_model option in Configuration File):

• \(\mathcal{G}(r) = r^n\): \(n\) can be any positive number. \(n=1\) (default value) is the theoretical value for a body wave in a homogeneous full-space; \(n=0.5\) is the theoretical value for a surface wave in a homogeneous half-space.

• Following Boatwright et al. [2002] (eq. 8), to account for the mixture of body waves, Lg waves and surface waves at regional distances (\(r < 200 km\)), a two-part geometrical spreading coefficient:

  • body wave spreading (\(\mathcal{G}(r) = r\)) for hypocentral distances below a cutoff distance \(r_0\);

  • frequency dependent spreading for hypocentral distances above the cutoff distance \(r_0\).

More precisely, the expression derived from Boatwright et al. [2002] is:

\[\begin{split}\mathcal{G}(r) = \begin{cases} r & r \le r_0\\ r_0 (r/r_0)^{\gamma (f)} & r > r_0 \end{cases}\end{split}\]

with

\[\begin{split}\gamma (f) = \begin{cases} 0.5 & f \le 0.20 Hz\\ 0.5 + 2 \log_{10} (5f) & 0.20 < f < 0.25 Hz\\ 0.7 & f \ge 0.25 Hz\\ \end{cases}\end{split}\]

Note that here we use the square root of eq. 8 in Boatwright et al. [2002], since we correct the spectral amplitude and not the energy.

For teleseismic distances (see the option geom_spread_min_teleseismic_distance in Configuration File), the geometrical spreading coefficient is defined as in Okal [1992] (eq. 4):

\[\mathcal{G}(\Delta) = \frac{a}{g(\Delta)}\]

where \(\Delta\) is the great circle distance between the source and the receiver, \(a\) is the Earth radius and \(g(\Delta)\) is defined as:

\[g(\Delta) = \left( \frac{\rho_h c_h}{\rho_r c_r} \frac{\sin i_h}{\sin \Delta} \frac{1}{\cos i_r} \left| \frac{d i_h}{d \Delta} \right| \right)^{1/2}\]

where \(\rho_h\) and \(\rho_r\) are the medium densities at the hypocenter and at the receiver, respectively, \(c_h\) and \(c_r\) are the P- or S-wave velocities at the hypocenter and at the receiver, respectively, \(i_h\) and \(i_r\) are the takeoff angle (hypocenter) and the incidence angle (receiver), respectively, and \(\frac{d i_h}{d \Delta}\) is the variation of the takeoff angle within a ray tube of width \(\Delta\) (see Okal [1992] for details).

Building spectra

In source_spec, the observed spectrum of component \(x\) (vertical or horizontal), \(S^{p|s}_x(f)\) is converted into moment magnitude units \(M_w\).

The first step is to multiply the spectrum for the geometrical spreading coefficient and convert it to seismic moment units:

\[M^{p|s}_x(f) \equiv \mathcal{G}(r) \times \frac{4 \pi \rho_h^{1/2} \rho_r^{1/2} c_h^{5/2} c_r^{1/2}} {2 R_{\Theta\Phi}} \times S^{p|s}_x(f) = M_O \times \frac{1}{1+\left(\frac{f}{f^{p|s}_c}\right)^2} \times e^{- \pi f t^*}\]

Then the spectrum is converted in units of magnitude:

\[Y^{p|s}_x(f) \equiv \frac{2}{3} \times \left( \log_{10} M^{p|s}_x(f) - 9.1 \right)\]

And the final data vector \(Y^{p|s}(f)\) is obtained by combining the three components (e.g., Z, N, E) through the root sum of squares:

\[Y^{p|s}(f) = \sqrt{ \left( Y^{p|s}_z(f) \right)^2 + \left( Y^{p|s}_n(f) \right)^2 + \left( Y^{p|s}_e(f) \right)^2 }\]

The data vector is compared to the theoretical model:

\[ \begin{align}\begin{aligned}Y^{p|s}(f) = \frac{2}{3} \left[ \log_{10} \left( M_O \times \frac{1}{1+\left(\frac{f}{f^{p|s}_c}\right)^2} \times e^{- \pi f t^*} \right) - 9.1 \right] =\\ = \frac{2}{3} \left( \log_{10} M_0 - 9.1 \right) + \frac{2}{3} \left[ \log_{10} \left( \frac{1}{1+\left(\frac{f}{f^{p|s}_c}\right)^2} \right) + \log_{10} \left( e^{- \pi f t^*} \right) \right]\end{aligned}\end{align} \]

Finally coming to the following model used for the inversion:

\[Y^{p|s}(f) = M_w + \frac{2}{3} \left[ - \log_{10} \left( 1+\left(\frac{f}{f^{p|s}_c}\right)^2 \right) - \pi \, f t^* \log_{10} e \right]\]

where \(M_w \equiv \frac{2}{3} (\log_{10} M_0 - 9.1)\).

Inverted parameters

The parameters determined from the spectral inversion are \(M_w\), \(f^{p|s}_c\) and \(t^*\).

The inversion is performed in moment magnitude \(M_w\) units (logarithmic amplitude). Different inversion algorithms can be used:

• TNC: truncated Newton algorithm (with bounds)

• LM: Levenberg-Marquardt algorithm (warning: Trust Region Reflective algorithm will be used instead if bounds are provided)

• BH: basin-hopping algorithm

• GS: grid search

• IS: importance sampling of misfit grid, using k-d tree

Other computed parameters

Starting from the inverted parameters \(M_0\) ( \(M_w\) ), \(f^{p|s}_c\), \(t^*\) and following the equations in Madariaga [2011] and Lancieri et al. [2012], other quantities are computed for each station:

• the static stress drop \(\Delta \sigma\)

• the source radius \(a\)

• the radiated energy \(E_r\)

• the apparent stress \(\sigma_a\)

• the quality factor \(Q_0\) of P- or S-waves

As a bonus, local magnitude \(M_l\) can be computed as well.

Event summaries (mean, weighted mean, percentiles) are computed from single station estimates. For mean and weighted mean estimation, outliers are rejected based on the interquartile range rule.

Source radius

The source radius is computed, assuming a circular rupture model, from the corner frequency \(f^{p|s}_c\) (Kaneko and Shearer [2014], equation 2):

\[a = k^{p|s} \frac{\beta_h}{f^{p|s}_c}\]

where \(\beta_h\) is the shear wave speed at the hypocenter (in \(m/s\)), \(f^{p|s}_c\) is the corner frequency (in \(Hz\)) estimated from the spectral inversion of P or S waves and \(k^{p|s}\) is a constant which depends on the source model.

Brune [1970] provides an expression for \(k^s\) in the case of a static circular crack (equation 31 in Madariaga [2011]):

\[k^s_{Brune} = 0.3724\]

Kaneko and Shearer [2014] compiled a table including their own values for \(k^p\) and \(k^s\) as well as values obtained from other authors. The values are given as a function of the rupture velocity \(V_r\) of a dynamic circular crack (or a static crack, when \(V_r\) is infinite):

Table 1 of Kaneko and Shearer [2014]

\(V_r/\beta_h\)	\(k^p_{K\&S}\)	\(k^s_{K\&S}\)	\(k^p_{Mada}\)	\(k^s_{Mada}\)	\(k^s_{Brune}\)	\(k^p_{S\&H}\)	\(k^s_{S\&H}\)
Infinite					0.3724
0.9	0.38	0.26	0.32	0.21		0.42	0.29
0.8	0.35	0.26				0.39	0.28
0.7	0.32	0.26				0.36	0.27
0.6	0.30	0.25				0.34	0.27
0.5	0.28	0.22				0.31	0.24

Where “K&S” stands for Kaneko and Shearer [2014], “Mada” for Madariaga [1976], and “S&H” for Sato and Hirasawa [1973].

Static stress drop

The static stress drop \(\Delta \sigma\) is computed under the assumption of a circular rupture of radius \(a\), as discussed in Madariaga [2011] (equation 27):

\[\Delta \sigma = \frac{7}{16} \frac{M_0}{a^3}\]

where \(M_0\) is the seismic moment (in \(N \cdot m\)) and \(a\) is the source radius (in \(m\)).

Radiated energy

The computation of the radiated energy \(E_r\) starts with the integral of the squared velocity amplitude spectrum: \([\dot{S}^{p|s}(f)]^2 = [ 2 \pi f S^{p|s}(f) ]^2\).

Following Boatwright et al. [2002] (equation 1) and Lancieri et al. [2012] (equation 3), the P- or S-wave radiated energy is computed as:

\[\tilde{\tilde{E}}_r^{p|s} = 8 \pi \mathcal{G}^2(r) C^2 \rho_r c_r \int_{f_{min}}^{f_{max}} e^{2 \pi f t^*} [\dot{S}^{p|s}(f)]^2 df\]

where \(\mathcal{G}^2(r)\) is the squared geometrical spreading coefficient (see above), \(C\) is a constant discussed below, \(\rho_r\) and \(c_r\) are, respectively, the density and P- or S-wave velocity at the receiver (their product is the seismic impedance), \(f_{min}\) and \(f_{max}\) are the minimum and maximum frequency used to compute the energy (see Configuration File for details on the Er_freq_range parameter), and the exponential term in the integrand is the squared correction for anelastic attenuation. The double tilde on top of \(\tilde{\tilde{E}}_r^{p|s}\) means that the radiated energy needs to be further corrected for noise and finite bandwidth (see below).

The constant \(C\) is defined in Boatwright et al. [2002] (equation 2) as:

\[C = \frac{\left<R_{\Theta\Phi}\right>}{R_{\Theta\Phi} F}\]

where \(\left<R_{\Theta\Phi}\right>\) is the root mean square P- or S-wave radiation pattern computed on the focal sphere, \(R_{\Theta\Phi}\) is the radiation pattern coefficient for the given station, and \(F\) is the free surface amplification factor. If a focal mechanism is not available, then it is assumed \(R_{\Theta\Phi} = \left<R_{\Theta\Phi}\right>\) and, hence, \(C = 1/F\). This assumption means that we rely on the averaging between measurements of radiated energy at different stations, instead of precise measurements at a single station.

Noise correction

To account for low frequency noise, below the corner frequency, under the hypothesis that energy is additive and that noise is stationary, we compute a noise-corrected energy as:

\[\tilde{E}^{p|s}_r = \tilde{\tilde{E}}^{p|s}_r - \tilde{\tilde{E}}^{noise}_r\]

where the first term is the radiated energy computed from the P- or S-wave spectrum and the second term is the radiated energy computed from the noise spectrum. If the above difference is negative, then the measure is rejected, since the noise is too large compared to the signal.

Finite bandwidth correction

Next step is to correct the radiated energy for the missing energy above \(f_{max}\), not taken into account in the integral of the squared velocity amplitude spectrum (finite bandwidth correction). Following Lancieri et al. [2012] (equation 4), and Di Bona and Rovelli [1988], the noise-corrected radiated energy is divided by the theoretical ratio \(R\) between the estimated radiated energy and the true radiated energy, defined as:

\[R = \frac{2}{\pi} \left[ \arctan(f_{max}/f^{p|s}_c) - \frac{f_{max}/f^{p|s}_c}{1+(f_{max}/f^{p|s}_c)^2} \right]\]

where \(f_{max}\) is the maximum frequency used to compute the energy integral and \(f^{p|s}_c\) is the P- or S-wave corner frequency.

The values of R range between 0 (for \(f_{max}/f^{p|s}_c \to 0\)) and 1 (for \(f_{max}/f^{p|s}_c \to \infty\)).

The corrected radiated energy for P- or S-waves is then:

\[E^{p|s}_r = \frac{\tilde{E}^{p|s}_r}{R}\]

Energy partition

The final step is to account for the partition of energy between P and S waves. Following Boatwright and Choy [1986] (equations 8 and 15) the ratio between the radiated energy measured from S-waves and the radiated energy measured from P-waves is:

\[\frac{E_r^s}{E_r^p} = 15.6\]

The final estimate of the radiated energy is then:

\[E_r = \left( 1 + 15.6 \right) E_r^p\]

or

\[E_r = \left( 1 + \frac{1}{15.6} \right) E_r^s\]

depending on whether the radiated energy is computed from P or S waves.

Apparent stress

The apparent stress \(\sigma_a\) is computed as (Madariaga [2011], eq. 18):

\[\sigma_a = \mu_h \frac{E_r}{M_0}\]

where \(\mu_h\) is the shear modulus (or rigidity, in \(Pa\)) near the hypocenter, \(E_r\) is the radiated energy (in \(N \cdot m\)), and \(M_0\) is the seismic moment (in \(N \cdot m\)).

The value of \(\mu_h\) is computed from the shear wave velocity (\(\beta_h\)) and the density (\(\rho_h\)) at the hypocenter, using the following expression:

\[\mu_h = \rho_h \beta_h^2\]

Quality factor

The retrieved attenuation parameter \(t^*\) is converted to the P- or S-wave quality factor \(Q_0^{p|s}\) using the following expression:

\[Q_0^{p|s} = \frac{tt^{p|s}(r)}{t^*}\]

where \(tt^{p|s}(r)\) is the P- or S-wave travel time from source to station and \(r\) is the hypocentral distance.

Station Residuals

Station-specific effects can be determined by running source_spec on several events and computing the average of station residuals between observed and inverted spectra. These averages are obtained through the command source_residuals; the resulting residuals file can be used for a second run of source_spec (see the residuals_filepath option in Configuration File).

Signal Processing

The following documentation explains, in chronological order, all the steps performed to construct the amplitude spectra used for the inversion.

Trace Processing

1. Traces are checked for gaps and overlaps. Traces with cumulative gap or overlap duration larger than gap_max or overlap_max, respectively, are skipped.

2. The trace mean is removed (only if the configuration parameter trace_units is set to auto, i.e., if the trace has not been preprocessed outside SourceSpec).

3. Gaps and overlaps are merged.

4. Traces with RMS smaller than rmsmin are skipped.

5. Traces are optionally checked for clipping (see Clipping Detection).

6. Instrumental response is removed and trace transformed in its physical units (e.g., velocity, acceleration).

7. Trace is filtered.

8. Signal to noise ratio is measured as the ratio between signal RMS in the P- or S-window (depending on the wave_type parameter) and the RMS of the noise window. Traces with signal to noise ratio smaller than sn_min are skipped.

See the source code of ssp_process_traces.process_traces() for implementation details.

Spectral Processing

1. A check is made to see if the signal window contains enough data. Traces with no data or more than 25% of zero values are skipped. This is typically due to a wrong specification of signal window (e.g., wrong P or S arrivals).

2. Traces are integrated to displacement in time domain, if time_domain_int is True.

3. Signal and noise windows are cut and tapered.

4. The noise window is checked. If the ratio between noise RMS and signal RMS is smaller than 1e-6, the noise is considered not significant and the trace is skipped.

5. The amplitude spectra of the signal and noise windows are computed, using numpy.fft.rfft(). If spectral_win_length is not None, the signal is zero-padded to this length before computing the Fast Fourier Transform.

6. Spectra are integrated to displacement in frequency domain, if time_domain_int is False.

7. Amplitude spectra are windowed (see config parameters freq1_broadb, freq2_broadb and similar).

8. Geometrical spreading is corrected (see Geometrical spreading).

9. Amplitude spectra are converted to seismic moment units (see Building spectra).

10. Amplitude spectra are resampled in \(log_{10}\) frequency spacing.

11. The resampled spectra are smoothed. The smoothing window width is defined in frequency decades (config parameter spectral_smooth_width_decades)

12. The spectral signal to noise ratio is checked. Amplitude spectra with signal to noise ratio smaller than spectral_sn_min are skipped.

13. The “H” component is built based on one or more spectral components, depending on the wave_type and ignore_vertical config parameters:

    • if wave_type is P or S:

      • if ignore_vertical is False, the two horizontals and the vertical components are combined;

      • if ignore_vertical is True, only the two horizontals components are combined;

    • if wave_type is SV:

      • if ignore_vertical is False, the radial and the vertical component are combined;

      • if ignore_vertical is True, only the radial component is used;

    • if wave_type is SH:

      • only the transverse component is used;

    Spectra are combined through the root sum of squares (see Overview).

14. All the amplitude spectra are converted to moment magnitude units (see Building spectra).

15. Station corrections are applied, if requested (see Station Residuals).

16. The weight spectrum is built, depending on the config option weighting.

See the source code of ssp_build_spectra.build_spectra() for implementation details.

Clipping Detection

SourceSpec can optionally check for clipping in the traces. Two algorithms are available for this purpose:

1. “Clipping Score” algorithm (default): compute a trace clipping score based on the shape of the kernel density estimation.

2. “Clipping Peaks” algorithm: check if trace is clipped, based on the number of peaks in the kernel density estimation;

Both algorithms are based on the kernel density estimation of the trace amplitude values, using the scipy.stats.gaussian_kde class. See the documentation of this class for more details on the kernel density.

The clipping detection algorithm is selected through the clipping_detection_algorithm parameter in the Configuration File. The algorithm options are set via the configuration file.

Debug plots cand be activated by setting the clipping_debug_plot parameter in the Configuration File to True.

A command line script (clipping_detection) is available to test the clipping detection algorithm on a set of traces. Run:

$ clipping_detection --help

for details on the script usage.

“Clipping Score” algorithm

The “Clipping Score” algorithm computes a trace clipping score based on the shape of the kernel density estimation of the trace amplitude values.

The algorithm is based on the following steps:

1. The trace is detrended and demeaned. Optionally, the trace baseline can be removed (if the configuration parameter remove_baseline is set to True)

2. A kernel density estimation is computed on the trace amplitude values.

3. Two weighted kernel density functions are computed:

   • a full weighted kernel density, where the kernel density is weighted by the distance from the zero mean amplitude value, using a 8th order power function between 1 and 100.

   • a weighted kernel density without the central peak, where the kernel density is weighted by the distance from the zero mean amplitude value, using a 8th order power function between 0 and 100.

   In both cases, the weight gives more importance to samples far from the zero mean value. In the second case, the central peak is ignored.

4. The score, ranging from 0 to 100, is the sum of the squared weighted kernel density without the central peak, normalized by the sum of the squared full weighted kernel density. The score is 0 if there is no additional peak beyond the central peak.

5. The trace is considered clipped if the score is above the clipping_score_threshold config parameter.

See the clipping_detection.clipping_score() function for more details.

Example debug plot for the “Clipping Score” algorithm. The score is the sum of the squared weighted kernel density without the central peak (shaded area), normalized by the sum of the squared full weighted kernel density (green curve). The largest the peaks far from the central peak, the higher the score.

“Clipping Peaks” algorithm

The “Clipping Peaks” algorithm checks if a trace is clipped, based on the number of peaks in the kernel density estimation of the trace amplitude values.

The algorithm is based on the following steps:

1. The trace is demeaned.

2. A kernel density estimation is computed on the trace amplitude values.

3. The kernel density estimation is weighted by the distance from the zero mean amplitude value, using a parabolic function, between 1 and 5.

4. Peaks are detected in the weighted kernel density estimation. The sensitivity of the peak detection algorithm is controlled by the clipping_peaks_sensitivity parameter, based on which a minimum prominence threshold is set.

5. The trace is considered clipped if there is at least one peak in the amplitude range corresponding to the clipping_peaks_percentile parameter.

See the clipping_detection.clipping_peaks() function for more details.

Example debug plot for the “Clipping Peaks” algorithm. If there is at least one peak in the amplitude range corresponding to the clipping_peaks_percentile (yellow areas), the trace is considered clipped.

Getting Started

For the impatient

Note

Note that the default config parameters are suited for a M<5 earthquake recorded within ~100 km. Adjust win_length, noise_pre_time, the frequency bands (bp_freqmin_*, bp_freqmax_*, freq1_*, freq2_*) and the bounds on fc and t_star, according to your problem.

Use case: miniSEED + StationXML + QuakeML

If you have seismic recordings in miniSEED format (e.g., traces.mseed), metadata in StationXML format (e.g., station.xml) and event information in QuakeML format (e.g., event.xml), then:

1. Generate a config file via source_spec -S;

2. Edit the config file variable station_metadata to point to station.xml file;

3. Run source_spec -t traces.mseed -q event.xml.

Use case: SAC + PAZ + SourceSpec Event File

If you have seismic recordings in SAC format (e.g., in a directory named sac_data), metadata as SAC polezero (PAZ) (e.g., in a directory named paz) and event information in any format, then:

1. Generate a config file via source_spec -S;

2. Edit the config file variable station_metadata to point to the paz directory;

3. Generate a sample SourceSpec Event File using source_spec -y; this will create a file named ssp_event.yaml;

4. Edit the file ssp_event.yaml with your event information;

5. Run source_spec -t sac_data -H ssp_event.yaml.

Command line arguments

After successfully installed SourceSpec (see Installation), you can get help on the command line arguments used by each code by typing from your terminal:

source_spec -h

(or source_model -h, or source_residuals -h).

source_spec and source_model require you to provide the path to seismic traces via the --trace_path command line argument (see File Formats).

Information on the seismic event can be stored in the trace header (SAC format), or provided through a QuakeML file (--qmlfile) or, alternatively (--hypocenter), through a SourceSpec Event File, a HYPO71 file, or a HYPOINVERSE-2000 file. See File Formats for more information on the supported file formats.

Configuration file

source_spec and source_model require a configuration file. The default file name is source_spec.conf, other file names can be specified via the --configfile command line argument.

You can generate a sample configuration file through:

source_spec -S

Take your time to go through the generated configuration file (named source_spec.conf): the comments within the file will guide you on how to set up the different parameters.

More details are in section Configuration File.

Configuration File

Configuration file (default name: source_spec.conf) is a plain text file with keys and values in the form key = value. Comment lines start with #.

Here is the default config file, generated through source_spec -S:

# Config file for source_spec

# GENERAL PARAMETERS --------
# All the fields are optional.
# The filled in fields will be written to output files.
# Author information
author_name = None
author_email = None
# Agency information
agency_full_name = None
agency_short_name = None
agency_url = None
# the logo can be a local file (it will be copied to the output dir)
# or a URL
agency_logo = None
# -------- GENERAL PARAMETERS

# TRACE AND METADATA PARAMETERS --------
# Channel naming for mis-oriented channels (vertical, horiz1, horiz2):
# Example:
#   mis_oriented_channels = Z,1,2
mis_oriented_channels = None

# Option to specify non standard instrument codes (e.g., "L" for accelerometer)
instrument_code_acceleration = None
instrument_code_velocity = None

# For more complex network.station.location.channel (SCNL) naming scenarios,
# you can provide a file, in json format, with traceid (SCNL) mapping
traceid_mapping_file = None

# List of traceids to ignore.
# Use network.station.location.channel; wildcards are accepted
# Example:
#   ignore_traceids = FR.CIEL.*.*, AM.RA0D3.00.*
ignore_traceids = None

# List of traceids to use.
# Use network.station.location.channel; wildcards are accepted
# Example:
#   use_traceids = FR.CIEL.*.*, AM.RA0D3.00.*
use_traceids = None

# Epicentral distance ranges (km) to select stations to be processed.
# Use a list of alternating min/max values, ex.:
#   to only use stations between 0 and 100 km:
#       epi_dist_ranges = 0, 100
#   to avoid teleseismic distances between 14° (1300 km) and 29° (3200 km)
#   where the P-wave undergoes travel time triplications:
#       epi_dist_ranges = 0, 1300, 3200, 999999
# Leave it to None to use all stations.
epi_dist_ranges = None

# Directory or single file name containing station metadata
# (instrument response and station coordinates).
# Note: this parameter can be overridden by the command line option
#       with the same name.
# Station metadata files can be in one of the following formats:
#   StationXML, dataless SEED, SEED RESP, PAZ (SAC polezero format)
# Notes:
# 1. SourceSpec will not enter in subdirectories of the given directory
#    (only one level allowed)
# 2. Traceid for PAZ files is specified through their name.
#    The traceid (network.station.location.channel) must be in the last four
#    fields (separated by a dot ".") before the file suffix (which can be
#    ".paz", ".pz", or no suffix).
#    Example:
#      PREFIX.NET.STA.LOC.CHAN.paz
#    or (no prefix):
#      NET.STA.LOC.CHAN.paz
#    or (no prefix and no suffix):
#      NET.STA.LOC.CHAN
# 3. If no traceid is specified through the PAZ file name, then it is assumed
#    that this is a generic PAZ, valid for all the stations that do not have
#    a specific PAZ. Use "trace_units" below to specify the units of the
#    generic PAZ.
# 4. SEED RESP and PAZ files do not contain station coordinates, which
#    should therefore be in the trace header (traces in SAC format)
station_metadata = None

# It is also possible to provide a constant sensitivity (i.e., flat instrument
# response curve) as a numerical value or a combination of SAC header fields
# (in this case, traces must be in SAC format).
# This parameter overrides the response curve computed from station_metadata.
# Leave it to None to compute instrument response from station_metadata.
# Examples:
#  sensitivity = 1
#  sensitivity = 1e3
#  sensitivity = resp0
#  sensitivity = resp1*resp2
#  sensitivity = user3/user2
sensitivity = None

# SQLite database file for storing output parameters (optional):
database_file = None

# Correct_instrumental_response (optional, default=True):
correct_instrumental_response = True

# Trace units.
# Leave it to 'auto' to let the code decide, based on instrument type.
# Manually set it to 'disp', 'vel' or 'acc' if you have already preprocessed
# the traces.
trace_units = auto
# -------- TRACE AND METADATA PARAMETERS


# TIME WINDOW PARAMETERS --------
# P and S wave velocity (in km/s) for travel time calculation
# (if None, the global velocity model 'iasp91' is used)
# Theoretical P or S arrival times are used when a manual P or S pick is not
# available, or when the manual P or S pick is too different from the
# theoretical arrival (see 'p_arrival_tolerance' and 's_arrival_tolerance'
# below).
vp_tt = None
vs_tt = None
# As an alternative, a directory containing NonLinLoc travel time grids
# can be specified and values defined above will be ignored.
# Note that reading NonLinLoc grids takes time. For simple 1D models, you
# can speed up considerably the process using a generic station
# named "DEFAULT". The coordinates of this default station are not important,
# since they will be superseded by each station's coordinates.
NLL_time_dir = None

# Arrival tolerances (in seconds) to accept a manual P or S pick
p_arrival_tolerance = 4.0
s_arrival_tolerance = 4.0

# Start time (in seconds) of the noise window, respect to the P window
# If None, it will be set to the length of the signal (P or S) window plus
# the value of "signal_pre_time" (see below)
noise_pre_time = 6.0

# Start time (in seconds) of the signal window, respect to the P or S arrival
# times (see "wave_type" below)
signal_pre_time = 1.0

# Length (in seconds) for both noise and signal windows
win_length = 5.0
# Variable window length factor (fraction of travel time):
#   win_length = max(win_length, variable_win_length_factor * travel_time)
# Set to None to disable variable window length.
variable_win_length_factor = None
# -------- TIME WINDOW PARAMETERS


# SPECTRUM PARAMETERS --------
# Wave type to analyse: 'P', 'S', 'SH' or 'SV'
# If 'SH' or 'SV' are selected, traces are rotated in the radial-transverse
# system. Transverse component is used for 'SH', radial component (and
# optionally the vertical component, see 'ignore_vertical' below) is used
# for 'SV'
wave_type = S

# Integrate in time domain (default: integration in spectral domain)
time_domain_int = False

# Ignore vertical components when building S or SV spectra
# Note: this option has no effect when 'wave_type' is 'P' (the vertical
# component is not ignored) and when 'wave_type' is 'SH' (the vertical
# component is not needed)
ignore_vertical = False

# Taper half width: between 0 (no taper) and 0.5
taper_halfwidth = 0.05

# Spectral window length (seconds)
# Signal is tapered, and then zero padded to
# this window length, so that the spectral
# sampling is fixed to 1/spectral_win_length.
# Comment out (or set to None) to use
# signal window as spectral window length.
spectral_win_length = None

# Spectral smoothing window width in frequency decades
# (i.e., log10 frequency scale).
# Example:
#  spectral_smooth_width_decades=1 means a width of 1 decade
#  (generally, too large, producing a spectrum which is too smooth).
#  spectrum(f0) is smoothed using values between f1 and f2, so that
#  log10(f1)=log10(f0)-0.5 and log10(f2)=log10(f0)+0.5
#    i.e.,
#  f1=f0/(10^0.5) and f2=f0*(10^0.5)
#    or,
#  f2/f1=10 (1 decade width)
# Default value of 0.2 is generally a good choice
spectral_smooth_width_decades = 0.2

# Residuals file path
# An HDF5 file with the mean residuals per station, used for station
# correction. This file is generally created using the command
# "source_residuals" on a previous SourceSpec run.
residuals_filepath = None

# Remove the signal baseline after instrument correction and before filtering
remove_baseline = False

# Band-pass frequencies (Hz) for accelerometers, velocimeters
# and displacement sensors.
# Use bp_freqmin_STATION and bp_freqmax_STATION to provide
# filter frequencies for a specific STATION code.
# TODO: calculate from sampling rate?
bp_freqmin_acc    = 1.0
bp_freqmax_acc    = 50.0
bp_freqmin_shortp = 1.0
bp_freqmax_shortp = 40.0
bp_freqmin_broadb = 0.5
bp_freqmax_broadb = 40.0
bp_freqmin_disp   = 0.5
bp_freqmax_disp   = 40.0

# Spectral windowing frequencies (Hz) for accelerometers, velocimeters
# and displacement sensors.
# (spectra will be cut between these two frequencies)
# Use freq1_STATION and freq2_STATION to provide
# windowing frequencies for a specific STATION code.
freq1_acc     = 1.0
freq2_acc     = 30.0
freq1_shortp  = 1.0
freq2_shortp  = 30.0
freq1_broadb  = 0.5
freq2_broadb  = 30.0
freq1_disp    = 0.5
freq2_disp    = 30.0

# Save the spectra to an HDF5 file in the output directory
save_spectra = False
# -------- SPECTRUM PARAMETERS


# SIGNAL/NOISE PARAMETERS --------
# Minimum rms (in trace units before instrument corrections)
# to consider a trace as noise
rmsmin = 0

# Time domain S/N ratio min
sn_min = 0

# Clipping detection algorithm
# Options:
#  - 'none': no clipping detection
#  - 'clipping_score': compute a clipping score for each trace, based on the
#    shape of the kernel density estimation of the trace amplitude values.
#    A high clipping score will be obtained for traces with a high number of
#    samples whose amplitude is close to the trace highest or lowest
#    amplitude values. Clipping scores for each trace are printed on the
#    terminal and in the log file.
#    Note: if "remove_baseline" is True (see above), clipping scores are
#    computed on the baseline-corrected signal.
#  - 'clipping_peaks': count the number of peaks in the kernel density
#    estimation of the trace amplitude values. The trace is considered clipped
#    if at least one peak is found within the trace highest or lowest amplitude
#    values. Kernel density peaks for each trace are printed on the terminal
#    and in the log file.
clipping_detection_algorithm = clipping_score
# Plot a debug figure for each trace with the results of the clipping algorithm
# Note: the figures are always shown, even if "plot_show" is False (see below)
clipping_debug_plot = False
# Threshold for the 'clipping_score' algorithm (between 0 and 100).
# A value of 100 means no clipping detection.
# This parameter is ignored if "clipping_detection_algorithm" is not set to
# 'clipping_score'.
clipping_score_threshold = 10
# Sensitivity for the 'clipping_peaks' algorithm (between 1 and 5).
# Higher values mean more peaks are detected.
# This parameter is ignored if "clipping_detection_algorithm" is not set to
# 'clipping_peaks'.
clipping_peaks_sensitivity = 3
# Trace amplitude percentile for the 'clipping_peaks' algorithm (between 0
# and 100). Example:
#   clipping_peaks_percentile = 10
# means that the 10% highest and lowest values of the trace amplitude will be
# checked for clipping.
# A value of 0 means that no clipping check will be performed.
# This parameter is ignored if "clipping_detection_algorithm" is not set to
# 'clipping_peaks'.
clipping_peaks_percentile = 10

# Maximum gap length for the whole trace, in seconds
gap_max = None
# Maximum overlap length for the whole trace, in seconds
overlap_max = None

# Minimum average spectral S/N ratio, below which a spectrum will be skipped
spectral_sn_min = 0
# Frequency range (Hz) to compute the average spectral S/N ratio
# (comment out or use None to indicate the whole frequency range)
# Example:
#  spectral_sn_freq_range = 0.1, 2
spectral_sn_freq_range = None
# -------- SIGNAL/NOISE PARAMETERS


# SPECTRAL MODEL PARAMETERS --------
# Free-surface amplification factor
free_surface_amplification = 2.0
# Layer top depths (km, positive down), for layered models (see below)
#  Note: generally, the first layer top depth should be 0 or a negative value
layer_top_depths = None
# P and S wave velocity close to the source (km/s)
# It can be a single value or a list of values (layered model)
# Set to None to use velocity from the global Earth model 'iasp91'
#   Note: specifying a layered model is useful when the same config file is
#   used for several SourceSpec runs with sources at different depths
vp_source = 5.5
vs_source = 3.2
# P and S wave velocity close to the stations (km/s)
# If set to None, velocity values close to the source will be used
# If set to None and velocity values close to the source are also set to None,
# then the global Earth model 'iasp91' will be used
vp_stations = None
vs_stations = None
# As an alternative, a directory containing a NonLinLoc velocity model can be
# specified. In this case, the values provided above will be ignored
NLL_model_dir = None
# Density close to the source (kg/m3)
# It can be a single value or a list of values (layered model)
# Set to None to use density from the global Earth model 'iasp91'
#   Note: specifying a layered model is useful when the same config file is
#   used for several SourceSpec runs with sources at different depths
rho_source = 2500
# Density close to the stations (kg/m3)
# If set to None, density value close to the source will be used
# If set to None and the density value close to the source is also set to None,
# then the global Earth model 'iasp91' will be used
rho_stations = None
# Geometrical spreading correction of wave amplitude.
# Spectra will be multiplied by this value to correct for the lost amplitude.
# Possible options are:
#    'r_power_n':   "r" to the power of "n" (rⁿ).
#                   You must provide the value of the exponent "n"
#                   (see "geom_spread_n_exponent" below).
#    'boatwright':  "r" (body waves) geometrical spreading for hypocentral
#                   distances below a cutoff distance; frequency-dependent
#                   geometrical spreading above the cutoff distance (Boatwright
#                   et al., 2002). You must provide the cutoff distance (see
#                   "geom_spread_cutoff_distance" below). This coefficient can
#                   be a valid choice for regional distances (up to 200 km),
#                   where S-waves, Lg waves and surface waves are mixed.
geom_spread_model = r_power_n
# Exponent "n" for the "r_power_n" geometrical spreading coefficient (positive
# float). Examples:
#   geom_spread_n_exponent = 1 (default, body wave in a homogeneous full-space)
#   geom_spread_n_exponent = 0.5 (surface wave in a homogeneous half-space)
geom_spread_n_exponent = 1
# Geometrical spreading cutoff hypocentral distance, in km, for the
# "boatwright" model:
geom_spread_cutoff_distance = 50
# Minimum epicentral distance (in km) to use a teleseismic geometrical
# spreading model. Above this distance, the model from Okal (1992) for body
# waves spreading in a spherically symmetric Earth will be used.
# Set to None to never use the teleseismic geometrical spreading model.
# Note that this model might not be appropriate for very deep events.
geom_spread_min_teleseismic_distance = None
# P-wave average radiation pattern coefficient:
rpp = 0.52
# S-wave average radiation pattern coefficient:
rps = 0.62
# Radiation pattern coefficient from focal mechanism, if available.
#   Note: radiation pattern is computed for the first arriving phase and might
#   not be correct for windows involving multiple phase arrivals (e.g.,
#   Lg waves, surface waves at regional distances, depth phases at teleseismic
#   distances)
rp_from_focal_mechanism = False
# "kp" and "ks" coefficients to compute source radius a from the P-wave
# corner frequency fc_p or the S-wave corner frequency fc_s and the shear
# wave speed beta ("vs_source"):
#
#   a = kp * beta / fc_p
#   a = ks * beta / fc_s
#
# (Madariaga, 2009; Kaneko and Shearer, 2014)
#
# The default value for S-waves is "ks = 0.3724", obtained by Brune (1970)
# for a static circular crack.
# Other values are discussed in Kaneko and Shearer (2014) for a dynamic
# circular crack, as a function of the ratio Vr/beta, where Vr is the rupture
# speed:
#
#   Vr/beta  kp(K&S)   ks(K&S)   kp(Mada)   ks(Mada)   kp(S&H)   ks(S&H)
#   0.9      0.38      0.26      0.32       0.21       0.42      0.29
#   0.8      0.35      0.26                            0.39      0.28
#   0.7      0.32      0.26                            0.36      0.27
#   0.6      0.30      0.25                            0.34      0.27
#   0.5      0.28      0.22                            0.31      0.24
#
#   K&S: Kaneko and Shearer (2014)
#   Mada: Madariaga (1976)
#   S&H: Sato and Hirasawa (1973)
kp = 0.38
ks = 0.3724
# -------- SPECTRAL MODEL PARAMETERS


# INVERSION PARAMETERS --------
# Weighting type: 'noise', 'frequency', 'inv_frequency' or 'no_weight'
#   'noise':         spectral signal/noise ratio weighting
#   'frequency':     a constant weight is applied for f<=f_weight
#                    a weight of 1 is used for f>f_weight
#                    (see "f_weight" and "weight" below)
#   'inv_frequency': weight is computed as 1/(f-f0+0.25)**0.25 for f<=f1,
#                    weight is 0 for f<f0 and f>f1.
#                    f0 and f1 are the first and last frequencies where
#                    spectral signal/noise ratio is above 3, or the first and
#                    last frequencies of the entire spectrum if no noise window
#                    is available
#   'no_weight':     no weighting
weighting = noise
# Parameters for 'frequency' weighting (ignored for the other weighting types):
#   weight for f<=f_weight (Hz)
#   1      for f> f_weight (Hz)
f_weight = 7.
weight = 10.

# Inversion algorithm:
# TNC: truncated Newton algorithm (with bounds)
# LM: Levenberg-Marquardt algorithm
# (warning: Trust Region Reflective algorithm will be used instead if
#  bounds are provided)
# BH: basin-hopping algorithm
# GS: grid search
# IS: importance sampling of misfit grid, using k-d tree
inv_algorithm = TNC

# Mw initial value and bounds.
# Set to True to use the magnitude (or scalar moment) from event file as
# initial Mw value for the inversion, instead of computing it from the average
# of the spectral plateau.
# If the event file does not contain a magnitude value or a scalar moment,
# then this parameter is ignored
Mw_0_from_event_file = False
# Allowed variability for Mw in the inversion
# (expressed as a fraction of Mw_0, between 0 and 1).
# This parameter is interpreted differently, depending on whether
# Mw_0_from_event_file is True or False:
#   - If Mw_0_from_event_file is True, then Mw_variability is interpreted as
#     the allowed variability around the Mw value provided in the event file.
#   - If Mw_0_from_event_file is False, then the Mw bounds are defined as
#       Mw_min = min(Mw(f))*(1-Mw_0_variability)
#       Mw_max = max(Mw(f))*(1+Mw_0_variability),
#     where Mw(f) is the low frequency spectral plateau in magnitude units.
#     If noise weighting is used, frequencies for which
#     S/N(f) < 0.5*max(S/N(f)) will be ignored, where S/N(f) is the spectral
#     signal to noise ratio.
Mw_0_variability = 0.1

# Bounds for fc (Hz)
# Specify bounds as a list, ex.:
#   fc_min_max = 0.1, 40
# Note:
#    If not specified, fc bounds will be autoset to fc0/10 and fc0*10, i.e. two
#    decades around fc0. The value of fc0 is set as the first maximum of
#    spectral S/N (noise weighting), or at "f_weight" (frequency weighting),
#    or at frequency where weight is 30% below the maximum (inverse-frequency
#    weighting) or at half of the frequency window (no weighting)
fc_min_max = None

# Initial value and bounds for t_star (seconds)
t_star_0 = 0.045
# Try to invert for t_star_0.
# If False, then the fixed t_star_0 defined above will be used.
# If the inverted t_star_0 is non-positive, then fixed t_star_0 will be used
invert_t_star_0 = False
# Allowed variability around inverted t_star_0 in the inversion
# (expressed as a fraction of t_star_0, between 0 and 1).
# If the inverted t_star_0 is non-positive, then t_star_min_max is used
# (see below).
t_star_0_variability = 0.1
# t_star_min_max does not supersede t_star_0_variability
t_star_min_max = 0.001, 0.25
# optional : Qo bounds (converted into t_star bounds in the code).
# (comment out or use None to indicate no bound)
# Note: if you want to explore negative t_star values, you have to specify
# -Qo_min, Qo_min. This because t_star is proportional to 1/Qo.
# Example, for searching only positive t_star values:
#   Qo_min_max = 10, 1000
# If you want to search also negative t_star values:
#   Qo_min_max = -10, 10
Qo_min_max = None
# -------- INVERSION PARAMETERS

# POST-INVERSION PARAMETERS --------
# Post-inversion bounds: use this bounds to reject certain inversion
# results, per station.
# Sometimes it is better to be more permissive with inversion parameters and
# reject "bad" solutions after the inversion, rather than forcing the
# inversion to converge within strict bounds.
# fc bounds, in Hz
pi_fc_min_max = None
# t_star bounds, in s
pi_t_star_min_max = None
# Static stress drop bounds, in MPa
pi_ssd_min_max = None
# Maximum acceptable misfit between inverted and observed spectrum
pi_misfit_max = None
# -------- POST-INVERSION PARAMETERS


# RADIATED-ENERGY PARAMETERS --------
# Minimum and maximum frequency (Hz) to measure radiated energy Er
# Examples:
#   Set min and max frequency to the "noise limits"
#   (i.e. the frequency range where spectral signal/noise ratio is above 3):
#       Er_freq_range = noise, noise
#   Use the whole spectrum:
#       Er_freq_range = None
#           or
#       Er_freq_range = None, None
#   Use the lowest possible frequency, and set the max frequency
#   to the "noise limit":
#       Er_freq_range = None, noise
#   Use frequencies between 1 and 10 Hz
#       Er_freq_range = 1, 10
#   Use frequencies between 1 and the "noise limit"
#       Er_freq_range = 1, noise
#
# The finite-band correction of Di Bona & Rovelli (1988) will be applied
# to account for the missing energy above the maximum frequency.
Er_freq_range = None, None
# -------- RADIATED-ENERGY PARAMETERS


# LOCAL MAGNITUDE PARAMETERS --------
compute_local_magnitude = False
# Local magnitude parameters:
#   ml = log10(A) + a * log10(R/100) + b * (R-100) + c
# where A is the maximum W-A amplitude (in mm)
# and R is the hypocentral distance (in km)
# Default values (for California) are:
#   a = 1., b = 0.00301, c = 3.
a = 1.
b = 0.00301
c = 3.
# Band-pass filtering frequencies (Hz) for local magnitude
ml_bp_freqmin = 0.1
ml_bp_freqmax = 20.0
# -------- LOCAL MAGNITUDE PARAMETERS


# SUMMARY STATISTICS PARAMETERS --------
# For each spectral parameter, SourceSpec computes three different summary
# estimates (from station estimates), using the following statistics:
#  - mean
#  - weighted_mean
#  - percentiles
# All the three summary estimates are stored in the YAML and SQLite output,
# but only a reference one is used for map plots, QuakeML and HYPO output,
# as well as for the "Event Summary" section in HTML report and for computing
# station spectral residuals.
# Use the parameter "reference_statistics" to specify the reference summary
# statistics that will be used in the cases described above.
reference_statistics = weighted_mean
# Number of sigmas (standard deviations) for average and weighted average
# uncertainty estimation
n_sigma = 1
# Percentage levels to compute lower, mid and upper percentiles
#   Example: to mimic a Gaussian distribution (one-sigma, 68.2% confidence):
#       lower_percentage = 15.9
#       mid_percentage = 50
#       upper_percentage = 84.1
# Note: the confidence level is upper_percentage - lower_percentage
lower_percentage = 15.9
mid_percentage = 50
upper_percentage = 84.1
# Reject outliers before computing means (standard and weighted),
# using the IQR method.
# IQR is the interquartile range Q3-Q1, where Q1 is the 25% percentile
# and Q3 is the 75% percentile.
# Values that are smaller than (Q1 - nIQR*IQR) or larger than (Q3 + nIQR*IQR)
# will be rejected as outliers.
# Set nIQR to None to disable outlier rejection.
# Note: this parameter also controls the position of "whiskers" on the source
# parameter box plots.
nIQR = 1.5
# -------- SUMMARY STATISTICS PARAMETERS


# PLOT PARAMETERS --------
# Show interactive plots (slower)
plot_show = False
# Save plots to disk
plot_save = True
# Save trace and spectrum plots as soon as they are ready.
# This uses less memory but slows down the code.
plot_save_asap = False
# Plot file format: 'png', 'pdf', 'pdf_multipage' or 'svg'
plot_save_format = png
# Plots an extra synthetic spectrum with no attenuation
plot_spectra_no_attenuation = False
# Plots an extra synthetic spectrum with no fc
plot_spectra_no_fc = False
# Max number of rows in plots
plot_spectra_maxrows = 3
plot_traces_maxrows = 3
# Plot ignored traces (clipped or low S/N)
plot_traces_ignored = True
# Plot ignored spectra (low S/N)
plot_spectra_ignored = True
# Plot station map
plot_station_map = False
# Map style (for regional maps)
#   Options: 'hillshade', 'hillshade_dark', 'ocean', 'satellite',
#            'stamen_terrain', 'no_basemap'
#   All basemap are from Esri, except 'stamen_terrain' which is from Stamen.
#   Notes:
#     1. The map style is only used for regional maps.
#        At teleseismic distances, the global map will alyaws use the
#        Natural Earth basemap.
#     2. For the 'stamen_terrain' basemap, you need a (free) API key from
#        Stadia Maps, see https://stadiamaps.com
plot_map_style = no_basemap
# API key for the 'stamen_terrain' basemap
#   Note: for privacy reasons, this parameter is not transcripted to the
#   output config file.
plot_map_api_key = None
# Plot station names on map
plot_station_names_on_map = False
# Text size for station names
plot_station_text_size = 8
# Coastline resolution
# Use None to let the code autoset the coastline resolution.
# Otherwise choose one of:
#   'full', 'high', 'intermediate', 'low', 'crude', 'no_coastline'
plot_coastline_resolution = None
# Zoom level for map tiles
# Use None to let the code autoset the zoom level
# Otherwise choose an integer between 1 (minimum zoom) and 18 (maximum zoom)
# Note: for zoom levels larger than 11, some map tiles could be missing
plot_map_tiles_zoom_level = None
# -------- PLOT PARAMETERS


# HTML REPORT --------
# Generate an HTML page summarizing the results of this run
# Note: "plot_save_format" (above) must be "png" or "svg"
html_report = False
# Link to event page. If set, the event ID on the HTML page will be a link to
# the event page. Use $EVENTID to indicate the current event ID.
# Example:
#   event_url = https://earthquake.usgs.gov/earthquakes/eventpage/$EVENTID/executive
event_url = None
# -------- HTML REPORT

# QUAKEML INPUT PARAMETERS --------
# Parameters for QuakeML input.
# Set "qml_event_description" to True, if you want to obtain the event name
# from the QuakeML event "description" tag
qml_event_description = False
# If "qml_event_description" is True, then the following parameter can be used
# to define a regular expression to extract the event name from the QuakeML
# event "description" tag.
# Examples:
#   - For QuakeML produced by https://api.franceseisme.fr, we want to keep
#     only the string "near of CITY NAME":
#        qml_event_description_regex = 'near of .+'
# Leave to None to use the full description as event name.
qml_event_description_regex = None
# -------- QUAKEML INPUT PARAMETERS


# QUAKEML OUTPUT PARAMETERS ----------------
# Parameters for QuakeML output.
#
# A QuakeML file will be generated only if QuakeML is used for input.
# The output file will be based on the input file, with additional information
# on seismic moment, Mw and source parameters computed by SourceSpec.
# Note: if you don't understand the parameters below, then probably you
# don't need QuakeML output and you can leave all the parameters to their
# default value

# Set SourceSpec Mw as preferred
set_preferred_magnitude = False
# Base for all the object ids (smi)
smi_base = "smi:local"
# String to strip from the Origin id when constructing the
# Magnitude and stationMagnitude ids.
smi_strip_from_origin_id = ""
# Template for the Magnitude object id (smi).
# Use $SMI_BASE to indicate smi_base defined above
# Use $ORIGIN_ID to indicate the id of the associated Origin.
smi_magnitude_template = "$SMI_BASE/Magnitude/Origin/$ORIGIN_ID#sourcespec"
# Template for the stationMagnitude object id (smi).
# Use $SMI_BASE to indicate smi_base defined above
# Use $ORIGIN_ID to indicate the id of the associated Origin.
# Use $SMI_MAGNITUDE_TEMPLATE to reuse the template for Magnitude object
# Use $WAVEFORM_ID to indicate the id of the associated waveform.
smi_station_magnitude_template = "$SMI_MAGNITUDE_TEMPLATE#$WAVEFORM_ID"
# Template for the MomentTensor object id (smi) which is used to store
# the scalar moment value.
# Use $SMI_BASE to indicate smi_base defined above
# Use $ORIGIN_ID to indicate the id of the associated Origin.
smi_moment_tensor_template = "$SMI_BASE/MomentTensor/Origin/$ORIGIN_ID#sourcespec"
# Template for the FocalMechanism object id (smi) which is used to store
# the scalar moment value.
# Use $SMI_BASE to indicate smi_base defined above
# Use $ORIGIN_ID to indicate the id of the associated Origin.
smi_focal_mechanism_template = "$SMI_BASE/FocalMechanism/Origin/$ORIGIN_ID#sourcespec"
# -----------------QUAKEML OUTPUT PARAMETERS

File Formats

Trace formats

SourceSpec can read all the trace formats supported by ObsPy.

Two very common choices are:

• miniSEED

• SAC

The SAC format can carry additional information in its header, like event location and origin time, phase picks, instrument sensitivity.

Input trace files can be provided –through the -t option– as a list of files, as a directory containing the files, or as a TAR(GZ) or ZIP archive containing the files.

Event formats

SourceSpec can read event information (event ID, location, origin time) in the following formats:

• SourceSpec Event File: this file can contain additional event information, such as magnitude, moment tensor or focal mechanism

• QuakeML: this file can contain additional event information, such as magnitude, moment tensor or focal mechanism. If phase picks are available, they will be read as well

• HYPO71

• HYPOINVERSE-2000: if phase picks are available, they will be read as well

Event information can also be stored in the SAC file header (header fields: EVLA, EVLO, EVDP, O, KEVNM).

Phase pick formats

Phase picks for P and S waves can be read from one of the following formats:

• QuakeML

• HYPO71

• HYPOINVERSE-2000

Phase picks can also be stored in the SAC file header, using the header fields A and T0 through T9. A pick label can be specified (header fields KA and KT0 through KT9) to identify the pick; the pick label can be a standard 4-characters SAC label (e.g., "IPU0", " S 1") or a label starting with "P" or "S" (lowercase or uppercase, e.g., "P", "pP", "Pg", "S", "Sn"). Picks with labels that cannot be parsed by SourceSpec will be ignored. If no label is specified, then SourceSpec will assume that A is the P-pick and T0 is the S-pick.

Station metadata formats

Station metadata (coordinates, instrumental response) can be provided in one of the following formats:

• StationXML

• Dataless SEED

• SEED RESP

• SAC polezero (PAZ)

Note that SEED RESP and PAZ formats do not contain station coordinates, which should therefore be in the trace header (traces in SAC format).

The station metadata file name or file directory is provided in the configuration file through the parameter station_metadata.

Alternatively, instrument sensitivity can be provided in the SAC header or as a constant in the configuration file. In both cases, use the configuration parameter sensitivity.

Output files

The SourceSpec main code, source_spec will produce the following output files (EVID is replaced by the actual event ID):

• EVID.ssp.yaml: YAML file containing the estimated spectral parameters (summary values and per station values)

• EVID.ssp.out (deprecated): text file containing the estimated spectral parameters (summary values and per station values)

• EVID.ssp.log: log file in text format (including the command line arguments, for reproducibility)

• EVID.ssp.conf: the input config file (for reproducibility)

• EVID.residuals.hdf5: station residuals in HDF5 File Format

• EVID.spectra.hdf5: (optional) spectra in HDF5 File Format

• EVID.ssp.h: hypocenter file in HYPO71 format with the estimated moment magnitude (only if an input HYPO71 file is provided)

• EVID.xml: updated QuakeML file with the results of the SourceSpec inversion (only if an input QuakeML file is provided)

The following plots will be created, in png, pdf or svg format:

• EVID.traces.png[.pdf,.svg]: trace plots

• EVID.ssp.png[.pdf,.svg]: spectral plots

• EVID.sspweight.png[.pdf,.svg]: spectral weight plots

• EVID.boxplot.png[.pdf,.svg]: box plots for the earthquake source parameters retrieved at each station

• Misfit plots, when using “grid search” or “importance sampling” for the spectral inversion

As an option, station maps can be created (requires Cartopy):

• EVID.map_mag.png[.pdf,.svg]: station map with symbols colored by estimated moment magnitude

• EVID.map_fc.png[.pdf,.svg]: station map with symbols colored by estimated corner frequency

As an option, the retrieved source parameters (per station and average) can be appended to a SQLite database, whose path is defined in the configuration file.

Finally, always as an option, source_spec can generate a report in HTML format.

SourceSpec Event File

The SourceSpec Event File format is a custom format, based on YAML, which allows specifying the source properties for one ore more given events. The following properties can be specified:

• event_id (mandatory): the event ID.

  Note

  This field must be preceded by a dash (-).

• name (optional): the event name

• hypocenter (mandatory): hypocentral location and origin time

• magnitude (optional): the event magnitude (used when Mw_0_from_event_file in the Configuration File is set to True)

  Note

  If a scalar moment or a moment tensor is given, a Mw magnitude will be (re)computed from it.

• scalar moment (optional): event scalar moment (used when Mw_0_from_event_file in the Configuration File is set to True)

  Note

  If a moment tensor is given, the scalar moment will be (re)computed from it.

• focal_mechanism (optional): event focal mechanism (used for computing radiation pattern when the option rp_from_focal_mechanism in the Configuration File is set to True).

  Note

  If a moment tensor is given, the focal mechanism will be (re)computed from it.

• moment_tensor (optional): event moment tensor (used for computing focal planes and radiation pattern when the option rp_from_focal_mechanism in the Configuration File is set to True; also used for computing moment magnitude, to be used when Mw_0_from_event_file is set to True)

See the sample SourceSpec Event File below for more details on the format.

Sample SourceSpec Event File

A sample SourceSpec Event File can be obtained through the command:

source_spec --samplesspevent

or

source_spec -y

The content of the sample SourceSpec Event File is shown below:

# SourceSpec Event File
#
# One or more events can be defined in this file.
# Each event must have a unique "event_id".
# Each event description starts with a dash (-) and is followed by a mandatory
# "event_id" field. The "hypocenter" field is mandatory, all the other fields
# are optional.
# Optional fields can be empty or commented out.
# The indentation of the fields must be respected.
#
# For a minimal working file, replace the placeholders between "<" and ">" with
# the appropriate values.
#
# This is a YAML file. For more information on the format, see
# https://en.wikipedia.org/wiki/YAML

# Mandatory event_id, preceded by a dash (-)
- event_id: <EVENT_ID>
  # Optional event name
  name:
  # Mandatory hypocenter information
  hypocenter:
    longitude:
      value: <LONGITUDE>
      # currently, only decimal degrees are supported
      units: deg
    latitude:
      value: <LATITUDE>
      # currently, only decimal degrees are supported
      units: deg
    depth:
      value: <DEPTH>
      # units can be one of the following: km, m
      units: km
    origin_time: <ORIGIN_TIME>
  # Optional magnitude value.
  # If a scalar moment or a moment tensor is given, a Mw magnitude will be
  # (re)computed from it
  magnitude:
    value:
    # magnitude type is a free string, e.g. Mw, mb, Ms, etc.
    mag_type:
  # Optional scalar moment.
  # If a moment tensor is given, the scalar moment will be (re)computed
  # from it
  scalar_moment:
    value:
    # units can be one of the following: N-m, dyne-cm
    units:
  # Optional focal mechanism, in terms of strike, dip and rake (in degrees)
  # of one of the two focal planes.
  # If a moment tensor is given, the focal mechanism will be (re)computed
  # from it
  focal_mechanism:
    # currently, only decimal degrees are supported
    units: deg
    strike:
    dip:
    rake:
  # Optional moment tensor, in up-south-east convention (USE)
  moment_tensor:
    # units can be one of the following: N-m, dyne-cm
    units:
    # moment tensor components, in moment units defined above
    m_rr:
    m_tt:
    m_pp:
    m_rt:
    m_rp:
    m_tp:

# You can specify as many events as you want, as long as they have unique
# event_id's
# - event_id: <EVENT_ID2>
#   hypocenter:
#     longitude:
#       value: <LONGITUDE2>
#       units: deg
#     latitude:
#       value: <LATITUDE2>
#       units: deg
#     depth:
#       value: <DEPTH2>
#       units: km
#     origin_time: <ORIGIN_TIME2>
# etc...

Spectral File Formats

Spectra produced by SourceSpec can be optionally saved in a HDF5 file named EVID.spectra.hdf5 (where EVID is the event ID; see the save_spectra option in Configuration File).

Additionally, spectral residuals are always saved to a file named EVID.residuals.hdf5, and average residuals produced by source_residuals are saved to a file named residual_mean.hdf5.

All these files use an HDF5 based format, detailed in the following section. They can be read using the function spectrum.read_spectra(), which returns a spectrum.SpectrumStream object, consisting of spectrum.Spectrum objects.

Example code:

>>> from sourcespec.spectrum import read_spectra
>>> specst = read_spectra('38471103.spectra.hdf5')
>>> print(specst)
SpectrumStream with 439 Spectrum objects:
CI.CCA..HHE | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
CI.CCA..HHH | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
CI.CCA..HHN | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
CI.CCA..HHS | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
CI.CCA..HHZ | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
CI.CCA..HHh | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
CI.CCA..HHs | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
...
>>> print(specst[0])
Spectrum CI.CCA..HHE | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced
>>> print(specst[0].stats)
{'delta': 0.1996007984031936,
 'npts': 200,
 'delta_logspaced': 0.040000000000000036,
 'npts_logspaced': 59,
 'station': 'CCA',
 'network': 'CI',
 'location': '',
 'channel': 'HHE',
 'azimuth': 198.9047069007039,
 'coeff': 1282817000215832.2,
 'coords': {'elevation': 0.71,
 'latitude': 35.15251922607422,
 'longitude': -118.01648712158203},
 ...

Additionally, spectrum.SpectrumStream and spectrum.Spectrum objects can be saved to a text file format, with one Spectrum per file. The details of this format are explained below.

Here is an example of how to read an HDF5 file and save it to a text file:

>>> from sourcespec.spectrum import read_spectra
>>> specst = read_spectra('38471103.spectra.hdf5')
>>> specst.write('38471103.spectra.txt', format='TEXT')
>>> from os import listdir
>>> print('\n'.join(sorted(listdir('.'))))
...
38471103.spectra_0000.txt
38471103.spectra_0001.txt
38471103.spectra_0002.txt
38471103.spectra_0003.txt
38471103.spectra_0004.txt
38471103.spectra_0005.txt
38471103.spectra_0006.txt
38471103.spectra_0007.txt
38471103.spectra_0008.txt
38471103.spectra_0009.txt
38471103.spectra_0010.txt
...
>>> specst0 = read_spectra('38471103.spectra_0000.txt', format='TEXT')
>>> print(specst0)
SpectrumStream with 1 Spectrum objects:
CI.CCA..HHE | 200 samples, 0.2-39.9 Hz | 0.2 Hz sample interval | 59 samples logspaced, 0.20-41.70 Hz | 0.04 log10([Hz]) sample interval logspaced

HDF5 File Format

In the HDF5 file format, all the spectra are stored in a group named spectra. This will allow for storing additional data types in the future. Within the spectra group, each spectrum.Spectrum object is stored in a group named spectrum_NNNNN_NET.STA.LOC.CHAN, where NNNN is the index of the spectrum in the original spectrum.SpectrumStream object. For each group, metadata is stored in the attributes section, and data is stored into 6 datasets, as illustrated below:

HDF5 ──> attributes
  └── spectra
      ├── spectrum_NNNNN_NET.STA.LOC.CHAN ──> attributes
      |   ├── data
      |   ├── data_logspaced
      |   ├── data_mag
      |   ├── data_mag_logspaced
      |   ├── freq
      |   └── freq_logspaced
      ├── spectrum_NNNNN_NET.STA.LOC.CHAN ──> attributes
      ...

The mandatory metadata fields are:

• network: the network code;

• station: the station code;

• location: the location code;

• channel: the channel code;

• delta: the sample interval for linearly spaced frequencies;

• npts: the number of samples for linearly spaced frequencies and data;

• delta_logspaced: the sample interval for logspaced frequencies (set to 1 if logspaced frequencies are not used);

• npts_logspaced: the number of samples for logspaced frequencies and data (set to 0 if logspaced frequencies are not used).

Other metadata fields might be present. Dictionary-like metadata fields are stored as YAML strings.

The 6 datasets are:

• data (mandatory): spectral amplitude;

• data_logspaced (optional): spectral amplitude for logspaced frequencies;

• data_mag (optional): spectral amplitude in magnitude units;

• data_mag_logspaced (optional): spectral amplitude in magnitude units for logspaced frequencies;

• freq (mandatory): linearly spaced frequencies;

• freq_logspaced (optional): logspaced frequencies.

Example code for reading a SourceSpec HDF5 file using h5py:

>>> import h5py
>>> fp = h5py.File('38471103.spectra.hdf5', 'r')
>>> spectra = fp['spectra']
>>> print('\n'.join(spectra.keys()))
spectrum_00000_CI.CCA..HHE
spectrum_00001_CI.CCA..HHH
spectrum_00002_CI.CCA..HHN
spectrum_00003_CI.CCA..HHS
...
>>> spec = spectra['spectrum_00000_CI.CCA..HHE']
>>> print(spec.attrs['network'])
CI
>>> print(spec.attrs['station'])
CCA
>>> print(spec.attrs['channel'])
HHE
>>> print(spec.attrs['coords'])
{'elevation': 0.959, 'latitude': 35.34149169921875, 'longitude': -116.87464141845703}
>>> print(spec['freq'][...])
[ 0.1996008   0.3992016   0.5988024   0.79840319  0.99800399  1.19760479
  1.39720559  1.59680639  1.79640719  1.99600798  2.19560878  2.39520958
...
>>> print(spec['data'][...])
[2.49035173e+15 1.37636948e+15 1.44746675e+15 1.63566457e+15
 6.93049162e+14 1.01315194e+15 9.36761128e+14 8.00776096e+14
...

TEXT File Format

The TEXT file format is not used internally by SourceSpec, but it can be useful to convert the HDF5 files to a more human-readable format (see above for an example on reading an HDF5 file and converting it to TEXT format).

The format is structured as follows:

• A header section in YAML format. The header is identified by the two lines # %BEGIN STATS YAML and # %END STATS YAML. Each line in the header starts with a # character, which should be removed when using a YAML parser.

• One or two data sections, each with three columns: frequency (Hz), data, data_mag (if data_mag is not present, it is set to nan):

  • linearly spaced data, between # %BEGIN LINSPACED DATA and # %END LINSPACED DATA;

  • (optional) logspaced data, between # %BEGIN LOGSPACED DATA and # %END LOGSPACED DATA.

Here’s an example TEXT file (with ellipses for brevity):

# %SOURCESPEC TEXT SPECTRUM FORMAT 1.0
# %BEGIN STATS YAML
# delta: 0.1996007984031936
# npts: 200
# delta_logspaced: 0.040000000000000036
# npts_logspaced: 59
# station: CCA
# network: CI
# location: ''
# channel: HHE
# azimuth: 198.9047069007039
# coeff: 1282817000215832.2
# coords:
#   elevation: 0.71
#   latitude: 35.15251922607422
#   longitude: -118.01648712158203
...
# %END STATS YAML
# %BEGIN LINSPACED DATA
# frequency(Hz) data data_mag
0.199601 60680538429002.429688 3.122033
0.399202 111489590392894.000000 3.298156
0.598802 109341550136192.765625 3.292523
0.798403 46532921128263.000000 3.045174
0.998004 69772021841544.039062 3.162454
...
# %END LINSPACED DATA
# %BEGIN LOGSPACED DATA
# frequency_logspaced(Hz) data_logspaced data_mag_logspaced
0.199601 60680538429002.437500 3.122033
0.218858 65978522113754.125000 3.146268
0.239973 71686845260565.812500 3.170293
0.263125 77968569379397.437500 3.194613
0.288511 84857989097347.140625 3.219128
...
# %END LOGSPACED DATA

Installation

SourceSpec requires at least Python 3.7. All the required dependencies will be downloaded and installed during the setup process.

Installing the latest release

Using Anaconda

The following command will automatically create an Anaconda environment named sourcespec, install the required packages and install the latest version of SourceSpec via pip:

conda env create --file https://raw.githubusercontent.com/SeismicSource/sourcespec/main/sourcespec_conda_env.yml

If you want a different name for your environment, use:

conda env create -n YOUR_ENV_NAME --file https://raw.githubusercontent.com/SeismicSource/sourcespec/main/sourcespec_conda_env.yml

Activate the environment with:

conda activate sourcespec

(or conda activate YOUR_ENV_NAME)

To keep SourceSpec updated run:

pip install --upgrade sourcespec

from within your environment.

Using pip and PyPI

The latest release of SourceSpec is available on the Python Package Index.

You can install it easily through pip:

pip install sourcespec

To upgrade from a previously installed version:

pip install --upgrade sourcespec

From SourceSpec GitHub releases

Download the latest release from the releases page, in zip or tar.gz format, then:

pip install sourcespec-X.Y.zip

or

pip install sourcespec-X.Y.tar.gz

Where, X.Y is the version number (e.g., 1.2). You don’t need to uncompress the release files yourself.

Installing a developer snapshot

If you need a recent feature that is not in the latest release (see the “unreleased” section in SourceSpec Changelog), you want to use the more recent development snapshot from the SourceSpec GitHub repository.

Using pip

The easiest way to install the most recent development snapshot is to download and install it through pip, using its builtin git client:

pip install git+https://github.com/SeismicSource/sourcespec.git

Run this command again, from times to times, to keep SourceSpec updated with the development version.

Cloning the SourceSpec GitHub repository

If you want to take a look at the source code (and possibly modify it 😉), clone the project using git:

git clone https://github.com/SeismicSource/sourcespec.git

or, using SSH:

git clone git@github.com:SeismicSource/sourcespec.git

(avoid using the “Download ZIP” option from the green “Code” button, since version number is lost).

Then, go into the sourcespec main directory and install the code in “editable mode” by running:

pip install -e .

You can keep your local SourceSpec repository updated by running git pull from times to times. Thanks to pip’s “editable mode”, you don’t need to reinstall SourceSpec after each update.

Sample Runs

Several sample runs are available in the sourcespec_testruns repository.

Getting Help

🙏 I need help

Join the SourceSpec Discussions and feel free to ask!

🐞 I found a bug

Please open an Issue.

Contributing

SourceSpec development happens on GitHub.

I’m very open to contributions: if you have new ideas, please open an Issue. Don’t hesitate sending me pull requests with new features and/or bugfixes!

How to Cite

If you used SourceSpec for a scientific paper, please cite it as:

Satriano, C. (2024). SourceSpec – Earthquake source parameters from P- or S-wave displacement spectra (X.Y). doi: 10.5281/ZENODO.3688587

Please replace X.Y with the SourceSpec version number you used.

You can also cite the following abstract presented at the 2016 AGU Fall Meeting:

Satriano, C., Mejia Uquiche, A. R., & Saurel, J. M. (2016). Spectral estimation of seismic moment, corner frequency and radiated energy for earthquakes in the Lesser Antilles. In AGU Fall Meeting Abstracts (Vol. 2016, pp. S13A-2518), bibcode: 2016AGUFM.S13A2518S

SourceSpec API

SourceSpec has a modular structure. Each module corresponds to a specific function or class of functions.

Main modules

SourceSpec main modules are presented below, following the logical order on which they’re used within source_spec.py.

source_spec.py

Earthquake source parameters from inversion of P- or S-wave spectra.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>, Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

source_spec.main()

Main routine for source_spec.

ssp_parse_arguments

Argument parser for sourcespec.

copyright:

2021-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_parse_arguments.parse_args(progname)

Parse command line arguments.

ssp_setup

Setup functions for sourcespec.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>, Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_setup.configure(options, progname, config_overrides=None)

Parse command line arguments and read config file.

Parameters:

• options (object) – An object containing command line options

• progname (str) – The name of the program

• config_overrides (dict) – A dictionary with parameters that override or extend those defined in the config file

Returns:

A Config object with both command line and config options.

ssp_setup.move_outdir(config)

Move outdir to a new dir named from evid (and optional run_id).

ssp_setup.remove_old_outdir(config)

Try to remove the old outdir.

ssp_setup.save_config(config)

Save config file to output dir.

ssp_setup.setup_logging(config, basename=None, progname='source_spec')

Set up the logging infrastructure.

This function is typically called twice: the first time without basename and a second time with a basename (typically the eventid). When called the second time, the previous logfile is renamed using the given basename.

ssp_setup.sigint_handler(sig, frame)

Handle SIGINT signal.

ssp_setup.ssp_exit(retval=0, abort=False)

Exit the program.

ssp_read_traces

Read traces in multiple formats of data and metadata.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>,

Sophie Lambotte <sophie.lambotte@unistra.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_read_traces.read_traces(config)

Read traces, store waveforms and metadata.

ssp_process_traces

Trace processing for sourcespec.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_process_traces.filter_trace(config, trace)

Filter trace.

ssp_process_traces.process_traces(config, st)

Remove mean, deconvolve and ignore unwanted components.

ssp_build_spectra

Build spectral objects.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>, Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

exception ssp_build_spectra.SpectrumIgnored(message, reason)

Spectrum ignored exception

ssp_build_spectra.build_spectra(config, st)

Build spectra and the spec_st object.

Computes P- or S-wave (displacement) spectra from accelerometers and velocimeters, uncorrected for anelastic attenuation, corrected for instrumental constants, normalized by geometrical spreading.

ssp_plot_traces

Trace plotting routine.

copyright:

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_plot_traces.ScalarFormatter(*args: Any, **kwargs: Any)

A ScalarFormatter with a custom format.

ssp_plot_traces.plot_traces(config, st, ncols=None, block=True)

Plot traces in the original instrument unit (velocity or acceleration).

Display to screen and/or save to file.

ssp_inversion

Spectral inversion routines for sourcespec.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>, Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_inversion.spectral_inversion(config, spec_st, weight_st)

Inversion of displacement spectra.

ssp_radiated_energy

Compute radiated energy from spectral integration.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>, Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_radiated_energy.radiated_energy_and_apparent_stress(config, spec_st, specnoise_st, sspec_output)

Compute radiated energy (in N.m) and apparent stress (in MPa).

Parameters:

• config – Config object

• type (sspec_output) – sourcespec.config.Config

• spec_st – Stream of spectra

• type – obspy.core.stream.Stream

• specnoise_st – Stream of noise spectra

• type – obspy.core.stream.Stream

• sspec_output – Output of spectral inversion

• type – sourcespec.ssp_data_types.SourceSpecOutput

ssp_local_magnitude

Local magnitude calculation for sourcespec.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_local_magnitude.local_magnitude(config, st, proc_st, sspec_output)

Compute local magnitude from max absolute W-A amplitude.

ssp_summary_statistics

Post processing of station source parameters.

copyright:

2012-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_summary_statistics.compute_summary_statistics(config, sspec_output)

Compute summary statistics from station spectral parameters.

ssp_output

Output functions for source_spec.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_output.save_spectra(config, spec_st)

Save spectra to file.

ssp_output.write_output(config, sspec_output)

Write results into different formats.

ssp_residuals

Spectral residual routine for sourcespec.

copyright:

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_residuals.spectral_residuals(config, spec_st, sspec_output)

Compute spectral residuals with respect to an average spectral model. Saves a stream of residuals to disk in HDF5 format.

Parameters:

• config (Config) – Configuration object

• spec_st (SpectrumStream) – Stream of spectra

• sspec_output (SourceSpecOutput) – Output of the source spectral parameter estimation

ssp_plot_spectra

Spectral plotting routine.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_plot_spectra.PlotParams

Parameters for plotting spectra.

set_plot_params(config, spec_st, specnoise_st)

Determine the number of plots and axes min and max.

ssp_plot_spectra.plot_spectra(config, spec_st, specnoise_st=None, ncols=None, stack_plots=False, plot_type='regular')

Plot spectra for signal and noise.

Display to screen and/or save to file.

ssp_plot_stacked_spectra

Plot stacked spectra, along with summary inverted spectrum.

copyright:

2023-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_plot_stacked_spectra.plot_stacked_spectra(config, spec_st, sspec_output)

Plot stacked spectra, along with summary inverted spectrum.

ssp_plot_params_stats

Plot parameter statistics.

copyright:

2022-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_plot_params_stats.PlotParam(name, unit, color)

A plot parameter.

ssp_plot_params_stats.box_plots(config, sspec_output)

Show parameter statistics through box plots.

ssp_plot_stations

Station plotting routine.

copyright:

2018-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_plot_stations.plot_stations(config, sspec_output)

Plot station map, color coded by magnitude or fc.

Parameters:

• config (config.Config) – Configuration object

• sspec_output (ssp_data_types.SourceSpecOutput) – SourceSpecOutput object

ssp_html_report

Generate an HTML report for source_spec.

copyright:

2021-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_html_report.HTMLtemplates

Class to hold paths to HTML templates.

ssp_html_report.html_report(config, sspec_output)

Generate an HTML report.

Other modules

These modules, in alphabetical order, are used by the main modules.

ssp_correction

Spectral station correction calculated from ssp_residuals.

copyright:

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_correction.station_correction(spec_st, config)

Correct spectra using station-average residuals.

Residuals are obtained from a previous run.

ssp_data_types

Classes for spectral inversion routines.

copyright:

2017-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_data_types.Bounds(config, spec, initial_values)

Bounds for bounded spectral inversion.

property bounds

Get bounds for minimize() as sequence of (min, max) pairs.

get_bounds_curve_fit()

Get bounds for curve-fit().

class ssp_data_types.InitialValues(Mw_0=None, fc_0=None, t_star_0=None)

Initial values for spectral inversion.

get_params0()

Get initial values as a tuple.

class ssp_data_types.OrderedAttribDict

An ordered dictionary whose values can be accessed as classattributes.

class ssp_data_types.SourceSpecOutput

The output of SourceSpec.

error_array(key, filter_outliers=False)

Return an array of errors (two columns) for the given key.

find_outliers(key, n)

Find outliers using the IQR method.

    Q1-n*IQR   Q1   median  Q3    Q3+n*IQR
                |-----:-----|
o      |--------|     :     |--------|    o  o
                |-----:-----|
outlier         <----------->            outliers
                     IQR

If n is None, then the above check is skipped. Nan and inf values are also marked as outliers.

mean_nobs()

Return a dictionary of number of observations used for computing mean.

mean_uncertainties()

Return a dictionary of mean uncertainties.

mean_values()

Return a dictionary of mean values.

outlier_array(key)

Return an array of outliers for the given key.

percentiles_nobs()

Return a dictionary of number of observations used for computing percentiles.

percentiles_uncertainties()

Return a dictionary of percentile uncertainties.

percentiles_values()

Return a dictionary of percentile values.

reference_summary_parameters()

Return a dictionary of reference summary parameters, each being a SummaryStatistics() object.

reference_uncertainties()

Return a dictionary of reference uncertainties.

reference_values()

Return a dictionary of reference values.

value_array(key, filter_outliers=False)

Return an array of values for the given key.

weighted_mean_nobs()

Return a dictionary of number of observations used for computing weighted mean.

weighted_mean_uncertainties()

Return a dictionary of weighted mean uncertainties.

weighted_mean_values()

Return a dictionary of weighted mean values.

class ssp_data_types.SpectralParameter(param_id, name=None, units=None, value=None, uncertainty=None, lower_uncertainty=None, upper_uncertainty=None, confidence_level=None, format_spec=None)

A spectral parameter measured at one station.

compact_uncertainty()

Return uncertainty in a compact form.

class ssp_data_types.StationParameters(station_id, instrument_type=None, latitude=None, longitude=None, hypo_dist_in_km=None, epi_dist_in_km=None, azimuth=None)

The parameters describing a given station (e.g., its id and location) and the spectral parameters measured at that station.

Spectral parameters are provided as attributes, using SpectralParameter() objects.

get_spectral_parameters()

Return a dictionary of spectral parameters.

class ssp_data_types.SummarySpectralParameter(param_id, name=None, units=None, format_spec=None)

A summary spectral parameter comprising one ore more summary statistics.

class ssp_data_types.SummaryStatistics(stat_type, value=None, uncertainty=None, lower_uncertainty=None, upper_uncertainty=None, confidence_level=None, lower_percentage=None, mid_percentage=None, upper_percentage=None, nobs=None, message=None, format_spec=None)

A summary statistics (e.g., mean, weighted_mean, percentile), along with its uncertainty.

compact_uncertainty()

Return uncertainty in a compact form.

ssp_event

SourceSpec event class and supporting classes.

copyright:

2023-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_event.SSPCoordinate(value=None, units=None, **kwargs)

SourceSpec coordinate class.

Stores a longitude or latitude value. Currently only degrees are supported.

class ssp_event.SSPDepth(value=None, units=None, **kwargs)

SourceSpec depth class.

property value_in_km

Return the depth value in km.

property value_in_m

Return the depth value in m.

class ssp_event.SSPEvent(event_dict=None)

SourceSpec event class.

from_event_dict(event_dict)

Initialize the event object from a event dictionary.

class ssp_event.SSPFocalMechanism(strike=None, dip=None, rake=None, units=None, **kwargs)

SourceSpec focal mechanism class.

Angles are in degrees.

from_moment_tensor(moment_tensor)

Initialize the focal mechanism object from a moment tensor.

Parameters:

moment_tensor (SSPMomentTensor) – moment tensor object

class ssp_event.SSPHypocenter(longitude=None, latitude=None, depth=None, origin_time=None, **kwargs)

SourceSpec hypocenter class.

class ssp_event.SSPMagnitude(value=None, mag_type=None, **kwargs)

SourceSpec magnitude class.

from_scalar_moment(scalar_moment)

Initialize the magnitude object from a scalar moment.

Parameters:

scalar_moment (SSPScalarMoment) – scalar moment object

class ssp_event.SSPMomentTensor(units=None, m_rr=None, m_tt=None, m_pp=None, m_rt=None, m_rp=None, m_tp=None, **kwargs)

SourceSpec moment tensor class.

to_N_m()

Convert the moment tensor to N-m, if necessary.

class ssp_event.SSPScalarMoment(value=None, units=None, **kwargs)

SourceSpec scalar moment class.

from_moment_tensor(moment_tensor)

Initialize the scalar moment object from a moment tensor.

Parameters:

moment_tensor (SSPMomentTensor) – moment tensor object

to_N_m()

Convert the scalar moment to N-m, if necessary.

ssp_grid_sampling

A class for sampling a parameter space over a grid.

Sampling can be performed by several approaches. The class provides optimal solutions, uncertainties and plotting methods.

copyright:

2022-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_grid_sampling.GridSampling(misfit_func, bounds, nsteps, sampling_mode, params_name, params_unit)

A class for sampling a parameter space over a grid.

Sampling can be performed by several approaches. The class provides optimal solutions, uncertainties and plotting methods.

property conditional_misfit

Compute conditional misfit along each dimension.

Conditional misfit is computed by fixing the other parameters to their optimal value.

property conditional_peak_widths

Find width of conditional misfit around its minimum.

grid_search()

Sample the misfit function by simple grid search.

kdtree_search()

Sample the misfit function using kdtree search.

property min_idx

Find the index of the minimum of the misfit function.

property params_err

Return optimal parameters uncertainties.

property params_opt

Return optimal parameters.

plot_conditional_misfit(config, label)

Plot conditional misfit for each parameter.

plot_misfit_2d(config, plot_par_idx, label)

Plot a 2D conditional misfit map.

property values

Return a meshgrid of parameter values.

property values_1d

Extract a 1D array of parameter values along one dimension.

ssp_grid_sampling.find_peak_width(x, peak_idx, rel_height, negative=False)

Find width of a single peak at a given relative height.

rel_height: float parameter between 0 and 1

0 means the base of the curve and 1 the peak value (Note: this is the opposite of scipy.peak_widths)

ssp_pick

SourceSpec pick class.

copyright:

2023-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_pick.SSPPick

A pick object.

ssp_spectral_model

Spectral model and objective function.

copyright:

2012 Claudio Satriano <satriano@ipgp.fr>

2013-2014 Claudio Satriano <satriano@ipgp.fr>,

Emanuela Matrullo <matrullo@geologie.ens.fr>, Agnes Chounet <chounet@ipgp.fr>

2015-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_spectral_model.callback(_)

Empty callback function for bounded inversion.

ssp_spectral_model.objective_func(xdata, ydata, weight)

Objective function generator for bounded inversion.

ssp_spectral_model.spectral_model(freq, Mw, fc, t_star, alpha=1.0)

Spectral model.

\[Y_{data} = M_w + \frac{2}{3} \left[ - \log_{10} \left( 1+\left(\frac{f}{f_c}\right)^2 \right) - \pi \, f t^* \log_{10} e \right]\]

see Theoretical Background for a detailed derivation of this model.

ssp_qml_output

QuakeML output for source_spec.

copyright:

2016-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement, Version 2.1 (http://www.cecill.info/index.en.html)

class ssp_qml_output.SSPContainerTag(*args: Any, **kwargs: Any)

Container for nested custom tags.

class ssp_qml_output.SSPExtra(*args: Any, **kwargs: Any)

Container for custom tags.

class ssp_qml_output.SSPTag(*args: Any, **kwargs: Any)

Custom tag object.

ssp_qml_output.write_qml(config, sspec_output)

Write QuakeML output.

ssp_radiation_pattern

Compute radiation pattern.

copyright:

2021-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_radiation_pattern.get_radiation_pattern_coefficient(stats, config)

Get radiation pattern coefficient.

ssp_radiation_pattern.radiation_pattern(strike, dip, rake, takeoff_angle, azimuth, wave)

Body wave radiation pattern.

From Lay-Wallace, page 340.

ssp_radiation_pattern.toRad(angle)

Convert angle from degrees to radians.

ssp_read_sac_header

Read metadata from SAC file headers.

copyright:

2023-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_read_sac_header.compute_sensitivity_from_SAC(trace, config)

Compute sensitivity from SAC header fields.

ssp_read_sac_header.get_event_from_SAC(trace)

Get event information from SAC header.

ssp_read_sac_header.get_instrument_from_SAC(trace)

Get instrument information from SAC header.

ssp_read_sac_header.get_picks_from_SAC(trace)

Get picks from SAC header.

ssp_read_sac_header.get_station_coordinates_from_SAC(trace)

Get station coordinates from SAC header.

ssp_read_station_metadata

Read station metadata in StationXML, dataless SEED, SEED RESP, PAZ (SAC polezero format).

copyright:

2012-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_read_station_metadata.PAZ(file=None)

Instrument response defined through poles and zeros.

property seedID

Return the seedID.

to_inventory()

Convert PAZ object to an Inventory object.

ssp_read_station_metadata.read_station_metadata(path)

Read station metadata into an ObsPy Inventory object.

Parameters:

path (str) – path to the station metadata file or directory

Returns:

inventory

Return type:

Inventory

ssp_sqlite_output

SQLite output for source_spec.

copyright:

2013-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_sqlite_output.write_sqlite(config, sspec_output)

Write SSP output to SQLite database.

Parameters:

• config (config.Config) – SSP configuration object

• sspec_output (ssp_data_types.SourceSpecOutput) – SSP output object

ssp_update_db

Update an existing SourceSpec database from a previous version.

copyright:

2013-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_update_db.update_db_file(db_file)

Update an existing SourceSpec database from a previous version.

Parameters:

db_file (str) – SQLite database file

ssp_util

Utility functions for sourcespec.

copyright:

2012-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class ssp_util.MediumProperties(lon, lat, depth_in_km, config)

Class to retrieve medium properties from config.

Parameters:

• lon (float) – Longitude (degrees).

• lat (float) – Latitude (degrees).

• depth_in_km (float) – Depth (km).

• config (Config) – Configuration object.

get(mproperty, where)

Get medium property (P- or S-wave velocity, density) at a given point from NLL grid, config or taup model.

Parameters:

• mproperty (str) – Property to be retrieved ('vp', 'vs' or 'rho').

• where (str) – Where to retrieve medium property ('source' or 'stations').

Returns:

Property value.

Return type:

float

get_from_config_param_source(mproperty)

Get medium property at the source from config parameter.

Parameters:

mproperty (str) – Property to be retrieved ('vp', 'vs' or 'rho').

Returns:

Property value.

Return type:

float

get_from_config_param_station(mproperty)

Get medium property at the station from config parameter.

Parameters:

mproperty (str) – Property to be retrieved ('vp', 'vs' or 'rho').

Returns:

Property value.

Return type:

float

get_from_taup(mproperty)

Get medium property (P- or S-wave velocity, density) at a given depth from taup model.

Parameters:

mproperty (str) – Property to be retrieved ('vp', 'vs' or 'rho').

Returns:

Property value.

Return type:

float

get_vel_from_NLL(wave)

Get velocity from NLL model.

Parameters:

wave (str) – Wave type ('P' or 'S').

Returns:

Velocity (km/s).

Return type:

float

to_string(mproperty, value)

Return a string with the property name and value.

Parameters:

• mproperty (str) – Property name. Must contain one of the following: 'vp', 'vs', 'rho', 'depth'

• value (float) – Property value.

Returns:

Property string.

Return type:

str

ssp_util.cosine_taper(signal, width, left_taper=False)

Apply a cosine taper to the signal.

ssp_util.geom_spread_boatwright(hypo_dist_in_km, cutoff_dist_in_km, freqs)

” Geometrical spreading coefficient from Boatwright et al. (2002), eq. 8.

Except that we take the square root of eq. 8, since we correct amplitude and not energy.

Parameters:

• hypo_dist_in_km (float) – Hypocentral distance (km).

• cutoff_dist_in_km (float) – Cutoff distance (km).

• freqs (numpy.ndarray) – Frequencies (Hz).

Returns:

Geometrical spreading correction (in m)

Return type:

numpy.ndarray

ssp_util.geom_spread_r_power_n(hypo_dist_in_km, exponent)

rⁿ geometrical spreading coefficient.

Parameters:

• hypo_dist_in_km (float) – Hypocentral distance (km).

• exponent (float) – Exponent.

Returns:

Geometrical spreading correction (in m)

Return type:

float

ssp_util.geom_spread_teleseismic(angular_distance, source_depth_in_km, station_depth_in_km, phase)

Calculate geometrical spreading coefficient for teleseismic body waves.

Implements eq (4) in Okal (1992) for a spherically symmetric Earth. This equations is derived from the conservation of the kinetic energy flux along a ray tube between the source and the receiver.

Parameters:

• angular_distance (float) – Angular distance (degrees).

• source_depth_in_km (float) – Source depth (km).

• station_depth_in_km (float) – Station depth (km).

• phase (str) – Phase type ('P' or 'S').

Returns:

Geometrical spreading correction (in m)

Return type:

float

ssp_util.mag_to_moment(magnitude)

Convert magnitude to moment.

ssp_util.moment_to_mag(moment)

Convert moment to magnitude.

ssp_util.quality_factor(travel_time_in_s, t_star_in_s)

Compute quality factor from travel time and t_star.

ssp_util.remove_instr_response(trace, pre_filt=(0.5, 0.6, 40.0, 45.0))

Remove instrument response from a trace.

Trace is converted to the sensor units (m for a displacement sensor, m/s for a short period or broadband velocity sensor, m/s**2 for a strong motion sensor).

Parameters:

• trace (Trace) – Trace to be corrected.

• pre_filt (tuple of four floats) – Pre-filter frequencies (None means no pre-filtering).

ssp_util.select_trace(stream, traceid, instrtype)

Select trace from stream using traceid and instrument type.

ssp_util.smooth(signal, window_len=11, window='hanning')

Smooth the signal using a window with requested size.

ssp_util.source_radius(fc_in_hz, vs_in_m_per_s, k_coeff=0.3724)

Compute source radius in meters.

Madariaga (2009), doi:10.1007/978-1-4419-7695-6_22, eq. 31 Kaneko and Shearer (2014), doi:10.1093/gji/ggu030, eq. 2, 15, 16

ssp_util.spec_minmax(amp, freq, amp_minmax=None, freq_minmax=None)

Get minimum and maximum values of spectral amplitude and frequency.

ssp_util.static_stress_drop(Mo_in_N_m, ra_in_m)

Compute static stress drop in MPa.

Madariaga (2009), doi:10.1007/978-1-4419-7695-6_22, eq. 27

ssp_util.station_to_event_position(trace)

Compute station position with respect to the event, in terms of hypocentral distance (km), epicentral distance (km), great-circle distance (degrees), azimuth and back-azimuth.

Values are stored in the trace stats dictionary.

ssp_util.toDeg(radians)

Convert radians to degrees.

ssp_util.toRad(degrees)

Convert degrees to radians.

ssp_wave_arrival

Arrival time calculation for sourcespec.

copyright:

2012-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

ssp_wave_arrival.add_arrival_to_trace(trace, phase, config)

Add arrival time, travel time and takeoff angle to trace for the given phase.

Uses the theoretical arrival time if no pick is available or if the pick is too different from the theoretical arrival.

config

Config class for sourcespec.

copyright:

2013-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class config.Config

Config class for sourcespec.

kdtree

Grid importance sampling using a k-d tree.

copyright:

2022-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class kdtree.KDTCell(extent, calc_pdf, min_cell_prob=0, ndiv=None, maxdiv=None)

A cell of a k-d tree.

divide(parts)

Divide the cell in parts parts along every dimension.

class kdtree.KDTree(extent, init_parts, calc_pdf, min_cell_prob=0.0, maxdiv=None)

A k-d tree.

divide()

Find the cell with highest probability and divide it in 2 parts along every dimension

get_pdf(deltas)

Calculate the probability density function (PDF) on a grid defined by deltas.

savefig

Save Matplotlib figure. Optimize PNG format using PIL.

copyright:

2022-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

savefig.savefig(fig, figfile, fmt, quantize_colors=True, **kwargs)

Save Matplotlib figure. Optimize PNG format using PIL.

spectrum

Define Spectrum and SpectrumStream classes, similar to ObsPy’s Trace and Stream.

Provides the high-level function read_spectra() to read SpectrumStream objects from HDF5 or TEXT files.

copyright:

2012-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class spectrum.AttributeDict(*args, **kwargs)

A dictionary that allows attribute-style access.

class spectrum.Spectrum(obspy_trace=None)

A class to handle amplitude spectra.

Parameters:

obspy_trace – An ObsPy Trace object to compute the spectrum from.

copy()

Return a copy of the spectrum.

property data

Return the array containing the amplitude spectrum.

property data_logspaced

Return the array containing the amplitude spectrum in logspaced frequencies.

property data_mag

Return the array containing the amplitude spectrum in mangitude units.

property data_mag_logspaced

Return the array containing the amplitude spectrum in logspaced frequencies in magnitude units.

property freq

Return the frequency axis of the spectrum.

property freq_logspaced

Return the logspaced frequency axis of the spectrum.

from_obspy_trace(trace)

Compute the spectrum from an ObsPy Trace object.

get_id()

Return the id of the spectrum.

property id

Return the id of the spectrum.

plot(**kwargs)

Plot the amplitude spectrum.

slice(fmin, fmax, nearest_sample=True, pad=False, fill_value=None)

Slice the spectrum between fmin and fmax.

Parameters:

• fmin – Minimum frequency.

• fmax – Maximum frequency.

• nearest_sample – If True, the slice will include the nearest frequency to fmin and fmax.

• pad – If True, the slice will be padded with the value of fill_value until fmin and fmax are included.

• fill_value – The value to use for padding.

Note:

Only the linear spaced frequencies, data and data_mag are sliced. If the original spectrum contains logspaced frequencies, data, and data_mag, those are not preserved in the sliced spectrum.

write(filename, format='HDF5', append=False)

Write the spectrum to a file.

Parameters:

• filename – The name of the file to write to.

• format – The format to use. One of ‘HDF5’ or ‘TEXT’. Default is ‘HDF5’.

• append – If True, append the spectrum to an existing file. Only valid for HDF5 format.

class spectrum.SpectrumStream(iterable=(), /)

A class to handle a collection of amplitude spectra.

append(spectrum)

Append a spectrum to the collection.

select(**kwargs)

Select a subset of the SpectrumStream.

sort(reverse=False)

Sort the SpectrumStream in place.

write(filename, format='HDF5')

Write the SpectrumStream to a file.

Parameters:

• filename – The name of the file to write to.

• format – The format to use. One of ‘HDF5’ or ‘TEXT’.

spectrum.read_spectra(pathname, format='HDF5')

Read a SpectrumStream from one ore more files.

Parameters:

• pathname – The pathname of the files to read. Can contain wildcards.

• format – The format to use. One of ‘HDF5’ or ‘TEXT’. Default is ‘HDF5’.

Returns:

The SpectrumStream object.

spectrum.signal_fft(signal, delta)

Compute the complex Fourier transform of a signal.

Parameters:

• signal – The signal to transform.

• delta – The sampling interval.

Returns:

The Fourier transform and the frequency axis.

clipping_detection

Check trace for clipping using kernel density estimation of the trace= amplitude values.

Two methods are available:

1. clipping_score(): compute a trace clipping score based on the shape of the kernel density estimation.

2. clipping_peaks(): check if trace is clipped, based on the number of peaks in the kernel density estimation;

copyright:

2023-2024 Claudio Satriano <satriano@ipgp.fr>,

Kris Vanneste <kris.vanneste@oma.be>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

clipping_detection.clipping_peaks(trace, sensitivity=3, clipping_percentile=10, debug=False)

Check if a trace is clipped, based on the number of peaks in the kernel density estimation of the trace amplitude values.

The algorithm is based on the following steps:

1. The trace is demeaned.

2. A kernel density estimation is computed on the trace amplitude values.

3. The kernel density estimation is weighted by the distance from the zero mean amplitude value, using a parabolic function, between 1 and 5.

4. Peaks are detected in the weighted kernel density estimation. The sensitivity of the peak detection algorithm is controlled by the sensitivity parameter, based on which a minimum prominence threshold is set.

5. The trace is considered clipped if there is at least one peak in the amplitude range corresponding to the clipping_percentile parameter.

Parameters:

• trace (obspy.core.trace.Trace) – Trace to check.

• sensitivity (int) – Sensitivity level, from 1 (least sensitive) to 5 (most sensitive). (default: 3). The sensitivity level controls the minimum prominence threshold used for peak detection in the kernel density estimation. See the scipy.signal.find_peaks() documentation for more details.

• clipping_percentile (float, between 0 and 100) – Percentile of trace amplitude range (expressed as percentage) to check for clipping. Default is 10, which means that the 10% highest and lowest values of the trace amplitude will be checked for clipping. A value of 0 means that no clipping check will be performed.

• debug (bool) – If True, plot trace, samples histogram and kernel density.

Returns:

• trace_clipped (bool) – True if trace is clipped, False otherwise.

• properties (dict) – Dictionary with the properties used for the clipping detection. The dictionary contains the following keys:

  • npeaks (int): number of peaks found in the kernel density

  • npeaks_clipped (int): number of peaks found in the kernel density that are considered clipped

  • peaks (numpy.ndarray): list of peaks found in the kernel density

  • prominences (numpy.ndarray): list of prominences of the peaks found in the kernel density

clipping_detection.compute_clipping_score(trace, remove_baseline=False, debug=False)

Compute a trace clipping score based on the shape of the kernel density estimation of the trace amplitude values.

The algorithm is based on the following steps:

1. The trace is detrended and demeaned. Optionally, the trace baseline can be removed.

2. A kernel density estimation is computed on the trace amplitude values.

3. Two weighted kernel density functions are computed:

   • a full weighted kernel density, where the kernel density is weighted by the distance from the zero mean amplitude value, using a 8th order power function between 1 and 100.

   • a weighted kernel density without the central peak, where the kernel density is weighted by the distance from the zero mean amplitude value, using a 8th order power function between 0 and 100.

   In both cases, the weight gives more importance to samples far from the zero mean value. In the second case, the central peak is ignored.

4. The score, ranging from 0 to 100, is the sum of the squared weighted kernel density without the central peak, normalized by the sum of the squared full weighted kernel density. The score is 0 if there is no additional peak beyond the central peak.

Parameters:

• trace (Trace) – Trace to check for clipping.

• remove_baseline (bool, optional) – If set, remove the trace baseline before computing the score.

• debug (bool, optional) – If set, plot the trace, the samples histogram, the kernel density (unweighted and weighted), the kernel baseline model, and the misfit. Default is False.

Returns:

clipping_score – Clipping score, in percentage.

Return type:

float

Note

Distorted traces (e.g., signals with strong baselines) can also get a high clipping score. To avoid this, the trace baseline can be removed.

A debug mode is available to plot the trace, the samples histogram, the kernel density (unweighted and weighted), the kernel baseline model, and the misfit.

clipping_detection.main()

Main function

plot_sourcepars

plot_sourcepars.py

1D or 2D plot of source parameters from a sqlite parameter file.

copyright:

2023-2024 Claudio Satriano <satriano@ipgp.fr>

license:

CeCILL Free Software License Agreement v2.1 (http://www.cecill.info/licences.en.html)

class plot_sourcepars.Annot(xdata, ydata, labels, yformat)

Annotate the plot with the evid, Mw and the value of the parameter.

class plot_sourcepars.Params(args)

Class to handle the parameters from a sqlite file.

filter(stamin=None, stamax=None, magmin=None, magmax=None, ssdmin=None, ssdmax=None, sigmaamin=None, sigmaamax=None)

Filter the parameters based on one or more conditions.

plot_Er_mw(hist=False, fit=False, nbins=None)

Plot the logarithm of the radiated energy vs the moment magnitude.

Parameters:

• hist (bool) – If True, plot a 2D histogram instead of a scatter plot.

• fit (bool) – If True, plot a linear regression of Er vs mw.

• slope (bool) – If True, also fit the slope of the linear regression.

plot_fc_mw(hist=False, fit=False, slope=False, nbins=None, wave_type='S')

Plot the logarithm of the corner frequency vs the moment magnitude.

Parameters:

• hist (bool) – If True, plot a 2D histogram instead of a scatter plot.

• fit (bool) – If True, plot a linear regression of fc vs mw.

• slope (bool) – If True, also fit the slope of the linear regression.

plot_hist(param_name, nbins=None, wave_type='S')

Plot a histogram of the given parameter.

plot_ssd_mw(hist=False, fit=False, nbins=None)

Plot the logarithm of static stress drop vs moment magnitude.

Parameters:

• hist (bool) – If True, plot a 2D histogram instead of a scatter plot.

• fit (bool) – If True, plot a linear regression of ssd vs mw.

• slope (bool) – If True, also fit the slope of the linear regression.

skip_events(idx)

Skip events with index idx.

plot_sourcepars.apparent_stress_curve_Er_mw(sigma_a, mu, mw)

Constant apparent stress curve in Er vs Mw.

Madariaga (2009), doi:10.1007/978-1-4419-7695-6_22, eq. 33., page 374.

plot_sourcepars.calc_r2(x, y, yerr, a, b)

Coefficient of determination.

plot_sourcepars.fc_mw_function(mw, a, b)

Function to fit fc vs Mw.

plot_sourcepars.mag_to_moment(mag, b=0.5)

Convert magnitude to moment.

The parameter b is used to change the slope of the fc-Mw stress drop curve.

The standard value of b is 0.5, which corresponds to a self-similar model.

plot_sourcepars.main()

Main function.

plot_sourcepars.parse_args()

Parse command line arguments.

plot_sourcepars.query_event_params_into_numpy(cursor, param, param_type, query_condition)

Query a parameter from the Events table and return it as a numpy array.

plot_sourcepars.run()

Run the script.

plot_sourcepars.stress_drop_curve_Er_mw(delta_sigma, mu, mw)

Constant stress drop curve in Er vs Mw.

Madariaga (2009), doi:10.1007/978-1-4419-7695-6_22, eq. 33., page 374.

plot_sourcepars.stress_drop_curve_fc_mw(delta_sigma, vel, mw, k=0.3724, b=-0.5)

Constant stress drop curve in fc vs Mw.

Obtained by combining the equation for stress drop:

delta_sigma = 7/16 * Mo / a^3

with the equation for source radius:

a = k * vel * (delta_sigma / fc)

where k is a coefficient discussed in Kaneko and Shearer (2014).

For the Brune source model, k=0.3724.

Parameters:

• delta_sigma (float) – Stress drop in MPa.

• vel (float) – P or S-wave velocity in km/s.

• mw (float) – Moment magnitude.

• b (float, optional) – The slope of the stress drop curve. Default is -0.5.

• k (float, optional) – Coefficient for the source radius. Default is 0.3724 (Brune source model).

Returns:

fc – Corner frequency in Hz.

Return type:

float

SourceSpec Changelog

Earthquake source parameters from P- or S-wave displacement spectra

Copyright (c) 2011-2024 Claudio Satriano satriano@ipgp.fr

v1.8 - 2024-04-07

This long overdue release brings many improvements, new features and bugfixes gradually introduced during the last year.

Release highlights:

• New file formats for events and spectra

• New configuration options to better specifify velocity and density models

• Better support for P-wave inversion and teleseismic events

• Support for radiation pattern correction from focal mechanism

• Option for travel time-based signal window length

• More options to control the calculation of source radius and stress drop

• Improved estimation of radiated energy

• New source parameter: apparent stress

• Fix for map tiles not plotted anymore

This release requires at least Python 3.7.

Warning: the SQLite database used by this version is not compatible with previous versions. You will need to upgrade your old database manually or using source_spec -u DATABASE_FILE_NAME.

Make sure to read the detailed Changelog below 👇

Input/output

• Introducing a new file format for providing event information (hypocentral location, magnitude, focal mechanism, moment tensor): the SourceSpec Event File.

• New HDF5 and TEXT file formats to store spectra

• Station residuals are now saved in an HDF5 spectrum file, instead of a pickle file

• New config file option save_spectra to save the spectra to an HDF5 file in the output directory

• Changes in the YAML output file:

  • bsd (Brune stress drop) parameter renamed to ssd (static stress drop)

  • Store in the event_info section the values of vp, vs and rho close to the hypocenter

  • Store in the inversion_info section the type of wave used for the inversion (P, S, SV or SH)

• Changes in the SQLite database (warning: these changes break compatibility with previous database versions):

  • bsd (Brune stress drop) parameter renamed to ssd (static stress drop)

  • Store the Stations table information on whether each parameter is an outlier (see #38)

  • Make place in the Stations table for station-level errors on radiated energy (even if they are currently not computed)

  • Store in the Events table the number of observations used for computing each summary parameter

  • Store in the Events table weighted means for radiated energy and local magnitude, even if those means are currently the same as the simple means, since those parameters do not have station-level errors defined

  • New columns for apparent stress in both Events and Stations tables

  • Store in the Events table the values of vp, vs and rho close to the hypocenter

  • Store in the Events table the type of wave used for the inversion (P, S, SV or SH)

• New command line option (-u or --updatedb) to update an existing database from a previous version

• Input files are now linked symbolically in the input_files subdirectory of the output directory (not implemented for Windows)

• New command line option (-R or --run_id_subdir) to use run_id (if defined) as a subdirectory of the event directory

• Print event info to console and to log file

• HTML report improvements:

  • Event name in the summary table, if available

  • Author, agency and run completion date in the summary table

  • SourceSpec version in the inversion information table

  • Link to input files

  • Information on the type of wave used for the inversion (P, S, SV or SH)

Processing

• Use all the available components to compute P-wave spectra (previously, only the vertical component was used)

• Possibility of specifying a free surface amplification factor different from 2

• Possibility of specifying a layered velocity and density model for the source

• Possibility of specifying a different density for the source and for the stations

• If density is not provided (i.e., it is None), use the density from the global velocity model “iasp91”

• Teleseismic geometrical spreading model (Okal, 1992)

• New weighting option based on inverse frequency, so that lower frequencies have larger weight in the inversion. If traces contain noise, weights will be set to zero where SNR < 3 (see #37)

• For weights computed from spectral S/N ratio (noise weighting), set to zero all the weights below 20% of the maximum weight, so that these weakly constrained parts of the spectrum are ignored in the inversion

• Possibility of using variable signal window lengths for each station as a function of the travel time of the P or S wave (see #48)

Inversion

• Possibility of using the magnitude (or scalar moment) provided in the event file as initial Mw value for the inversion

• Reintroduced the possibility of providing the variability around the initial Mw value

• By combining the previous options, it is now possible to fix the Mw value during the inversion to the value provided in the event file

Post-Inversion

• Possibility of choosing the “k” coefficient to compute source radius from corner frequency (Kaneko and Shearer, 2014)

• Better control on the frequency range used for computing radiated energy (see #49)

• Use station-specific radiation pattern (when available) for computing radiated energy

• Take into account for energy partition when computing radiated energy (Boatwright and Choy, 1986). This affects mostly the radiated energy computed from P waves

• New source parameter: apparent stress

• For parameters with no station-level uncertainty defined (currently, radiated energy and local magnitude), use simple mean when computing summary weighted averages (the previous behavior was to not compute weighted averages for these parameters)

Plotting

• Show the station radiated energy (Er) value on the station spectra plots

• Show the summary radiated energy (Er) value on the stacked spectra plot

• Station maps improvements:

  • Possibility of choosing a basemap style or no basemap

  • Possibility of not plotting the coastlines

  • Exclude outliers when computing colorbar limits

  • Improved computation of bounding box for regional and teleseismic events

  • Use a global orthographic projection when using stations at large teleseismic epicentral distances (more than 3000 km)

• Changes to plot_sourcepars:

  • Read vp, vs and rho from the SQLite database (previously: vs was hardcoded to 3.5 km/s, rho to 2700 kg/m3 and vp was not used)

  • Read the source radius “k” coefficient from the SQLite database (previously: “k” was hardcoded to 0.3724, value for the Brune model)

  • New command line option --wave_type to select the wave type (P, S, SV or SH) for plots involving the corner frequency

  • Possibility of plotting histogram of apparent stress

  • Option to filter events by apparent stress

Config file

• New config parameter epi_dist_ranges to select stations within one or more ranges of epicentral distances. It replaces the old parameter max_epi_dist.

• New config parameter free_surface_amplification to specify the free surface amplification factor (default: 2)

• New config parameter layer_top_depths to specify the depth of the top of the layers in a layered velocity and density model

• The config parameters vp_source, vs_source and rho_source can now be lists of values, to specify a layered velocity and density model for the source

• Config parameter rho renamed to rho_source

• New config parameter rho_stations

• New config parameter geom_spread_min_teleseismic_distance to set the minimum epicentral distance for using the teleseismic geometrical spreading model

• New config parameters kp and ks to set the “k” coefficient for computing source radius from corner frequency

• Config parameter pi_bsd_min_max renamed to pi_ssd_min_max

• New option inv_frequency for the config parameter weighting (see #37)

• Config parameter max_freq_Er replaced by Er_freq_range (see #49)

• New parameters, qml_event_description and qml_event_description_regex, to obtain the event name from the QuakeML event “description” tag

• New parameter Mw_0_from_event_file to use the magnitude (or scalar moment) provided in the event file as initial Mw value for the inversion

• Reintroduced the parameter Mw_0_variability to set the variability around the initial Mw value

• New parameter plot_save_asap to save plots as soon as they are ready. This uses less memory but slows down the code.

• New parameter plot_map_style to choose the map style

• New parameter plot_map_api_key to provide a Stadia Maps api key for Stamen Terrain basemap

• New option for the parameter plot_coastline_resolution: no_coastline

• New config parameter variable_win_length_factor to specify window length as a fraction of the travel time of the P/S wave (see #48)

Bugfixes

• Fix source radius computation when using P waves (use P-wave velocity instead of S-wave velocity)

• Do not ignore picks labeled with lowercase “p” or “s”

• Fixed: config parameter p_arrival_tolerance was used also for S waves, instead of s_arrival_tolerance (see #35)

• Fix Boatwright spreading model (log10 instead of natural log)

• Fix bug where signal and noise windows were plotted with the wrong length, under certain circumstances (see #35)

• Fixes related to records with short signal windows (see #39)

• Fix for beachball not plotted anymore with recent versions of Matplotlib.

• Fix bug where traces ignored because of low spectral S/N ratio, where still plotted as if they were valid traces

• Fix bug when specifying an absolute path for output directory: the path was treated as relative (see #40)

• Fix bug where paths starting with tilde (~) were not parsed correctly (see #43 and #44)

• Fix bug where local magnitude was not written to the HYPO71 output file, when using weighted mean as reference statistics

• Fix for Stamen Terrain basemap now requiring an API key from Stadia Maps

Requirements

• Python minimum version raised to 3.7

• Matplotlib minimum version raised to 3.2

• Cartopy minimum version raised to 0.21

v1.7 - 2023-03-31

This release improves trace processing through the use of modern routines for instrument correction, optional baseline removal, a new clipping detection algorithm and a better definition of signal and noise time windows.

A new plot is introduced, “stacked spectra” which allows to compare all the spectra at once (and easily detect problematic stations 😉). Also, a new command line tool, plot_sourcepars, allows making aggregate plots of source parameters for many events (starting from the SQLite database).

New config file parameters have been added, while some have been removed. Please run source_spec -U CONFIG_FILE_NAME to update your old config file.

As always, many bugfixes and improvements have been made in this release. Thanks to all the users who took time to write and ask questions (by mail or using the official SourceSpec Discussions).

Big kudos to Kris Vanneste @krisvanneste who helped all along the development with code review and testing and submitted pull requests on noise windows and clipping detection.

Below is the detailed Changelog 👇

v1.7: Input/output

• Possibility of using a single PAZ file as a “generic” PAZ file for all the stations

• Command line option --station_metadata (or -w) for overriding the config file parameter with the same name (see pull request #16)

• Removed command line option --no-response for avoiding removing instrument response (use the config option correct_instrumental_response instead)

• New output file in YAML format. The old .out file is still available but deprecated.

• Information on the inversion procedure in YAML and HTML output

• Option to add an agency logo to the HTML page

• Possibility of generating HTML report without figures (see #30)

v1.7: Processing

• Use modern ObsPy trace.remove_response() routine for instrument correction (see #27)

• Option to remove the trace baseline after instrument correction and before filtering (remove_baseline config parameter) (see #25)

• New algorithms for clipping detection based on kernel density estimation of the trace amplitude values (see #23, #24, #25)

  • Two methods are available:

    • clipping_score: compute a trace clipping score based on the shape of the kernel density estimation.

    • clipping_peaks: check if trace is clipped, based on the number of peaks in the kernel density estimation;

  • Use clipping_detection_algorithm in the config file to choose the algorithm and the other clipping_* parameters to adjust the results.

  • The algorithms can also be called from the command line, e.g. for debug purposes, using the shell command clipping_detection.

• Relax noise window requirements if noise weighting is not used. This is useful for older triggered records with noise windows that are short or even missing entirely (see pull request #18)

• Some small improvements were made in the window definitions (see pull request #18):

  • Generate error if signal window is incomplete (P- or S-arrival before the start time of the trace)

  • Warn if noise window overlaps with P-window (instead of S-window, as in previous versions)

  • Constrain signal_pre_time for S-phase to half the S-P interval, if this interval is shorter than signal_pre_time (i.e., for short-distance records with short S-P interval)

• Extract source and station P and S velocities from global ‘iasp91’ velocity model, if both v(p,s)_source and v(p,s)_stations are set to None (see #20)

• Magnitude limits for inversion are now autoset between 90% of the minimum of the spectral plateau and 110% of its maximum (see #22)

v1.7: Post-Inversion

• Possibility of choosing the reference summary statistics that will be used for map plots, QuakeML and HYPO output, as well as for the “Event Summary” section in HTML report and for computing station spectral residuals. Available summary statistics are:

  • mean

  • weighted_mean

  • percentiles (new!)

• Possibility of defining the number of sigmas for uncertainties on event means and weighted means

v1.7: Plotting

• New plot: stacked spectra

• Do not zero-pad traces to common length when plotting, so that missing data at beginning or at the end can be easily detected (see #21)

• Plot noise and signal windows separately for each component (see #21)

• Show on the trace plot the reason why a trace has been ignored

• Logscale for boxplots, if parameters span a large interval (see pull request #15)

• Support for SVG format for plot files (can be used in HTML output as alternative to PNG)

• Improved trace plot quality for vector formats (PDF, SVG)

• New command line tool: plot_sourcepars to make 1D or 2D plot of source parameters from a sqlite parameter file.

v1.7: Config file

• Removed sensitivity_only option from correct_instrumental_response

• Removed config parameter: Mw_0_variability

• Removed config parameter: clip_max_percent

• New config parameter: remove_baseline

• New config parameters for clipping detection:

  • clipping_detection_algorithm

  • clipping_debug_plot

  • clipping_score_threshold

  • clipping_peaks_sensitivity

  • clipping_peaks_percentile

• Config file section AVERAGES PARAMETERS renamed to SUMMARY STATISTICS PARAMETERS

• New config parameter: reference_statistics

• New config parameter: n_sigma

• New config parameters for percentiles calculation: lower_percentage, mid_percentage and upper_percentage

• New config parameters for filtering and spectral windowing of displacement signals:

  • bp_freqmin_disp, bp_freqmax_disp

  • freq1_disp, freq2_disp

• Default values for t_star_min_max (instead of None)

v1.7: Code improvements

• Large refactoring of the whole codebase, to make the code more modern and easier to maintain (see #28)

v1.7: Bugfixes

• Properly ignore vertical components when ignore_vertical is True

• Fix a bug preventing reading phase picks from HYPOINVERSE-2000 files

• Fix for noise window not showing up in PNG trace plots in some cases

• Fix reading velocities from NLL model (see #20)

• HTML report: better scrollbars for station table across all the browsers

• Fix for cropped map for very large station-to-event distances (greater than 500 km)

• Fix a bug in generating evid form origin time when reading origin time from SAC header and the number of seconds was 59

• Fix a crash when no map tiles were available at the selected zoom level

• Fix for a corner case where the three components of the same instrument have different trace length (see #31)

• Fix source_residuals, which didn’t work anymore

v1.6 - 2022-08-02

This release introduces several modifications to the config file. You will need to upgrade your old config files manually or using source_spec -U CONFIG_FILE_NAME.

This release requires at least Python 3.6.

A lot of effort has been devoted to improve the documentation. Please check it out on https://sourcespec.readthedocs.io/

v1.6: Input/output

• QuakeML output (when using QuakeML input)

• Command line option --run-id to provide a string identifying the current run (see pull request #6)

• Write SourceSpec version to parfile

• Write SourceSpec version and run complete time to SQLite file

• Write author and agency info (if specified) to output files, figures and HTML report

• HTML page for misfit plots (when using grid search or importance sampling)

• Station table in HTML report is now sortable (and its header remains fixed)!

• Reduce PNG figures file size, while improving their resolution 😃

• Removed option to read event information and traces from a pickle file (rarely used)

v1.6: Processing

• Support for P-wave spectral inversion (see pull request #9)

• It is now possible to provide different vp and vs velocities, close to the source and close to the stations (see the new config options above and issue #5)

• Possibility to choose a geometrical spreading model between (see issue #8):

  • rⁿ (default: n=1 – body waves)

  • Boatwright et al. (2002): “r” below a cutoff distance, frequency-dependent above the cutoff distance

• Use travel time to compute quality factor from t* (and viceversa) (see issue #5)

• Compute travel time from pick and origin time, when possible (see issue #10)

• Warn if noise window ends after P or S arrival

v1.6: Post-Inversion

• Subtract the integral of noise spectrum from the integral of signal spectrum when computing radiated energy, under the hypothesis that energy is additive and noise is stationary

v1.6: Config file

• Config parameter paz has been removed and merged into station_metadata

• Config parameters vp and vs have been renamed to vp_source and vs_source (see issue #5)

• New, optional, config parameter vp_stations and vs_stations

• Config parameter pre_p_time and pre_s_time have been renamed to noise_pre_time and signal_pre_time, respectively (see pull request #9)

• Config parameter rps_from_focal_mechanism renamed to rp_from_focal_mechanism (see pull request #9)

• New config parameter: geom_spread_model (see issue #8)

• Config parameters PLOT_SHOW, PLOT_SAVE and PLOT_SAVE_FORMAT are now lowercase (plot_show, plot_save and plot_save_format)

• New, optional, general config parameters for specifying author and agency information. This information is written to output files and figures, if specified

• New config parameter, event_url, to link the event page from the HTML report

• Removed DEBUG config parameter

• Parameters from GENERAL PARAMETERS section reorganized into a new section called TRACE AND METADATA PARAMETERS

• Some parameters from INVERSION PARAMETERS moved into a new section called SPECTRAL MODEL PARAMETERS

v1.6: Bugfixes

• Fix for not working weighting options: frequency and no_weight

• Fix for negative weights occasionally generated by interpolation

• Fix bug when event coordinates are written into sqlite as binary blobs

v1.5 - 2022-05-22

This is a pretty big release coming after several months of work on a large dataset of more than 5000 events in Mayotte.

Please read through the changelog to discover all the improvements and new features.

You will need to update your old config files via source_spec -U CONFIG_FILE_NAME

Note that v1.5 is no more compatible with Python 2!

v1.5: Input/output

• Write output files into a subdirectory of OUTDIR, whose name is the event id

• Support for HYPOINVERSE-2000 output files

• Removed autodetection of hypo71 file paths (specific to CRL case)

• SQLite output: added radiated energy, weighted averages, errors on parameters, number of observations, hypocentral location and origin time

• Removed -C argument to apply station correction to spectra. Now spectra are automatically corrected if residuals_filepath is specified in the configuration file

• Save an additional event parameter to output files: average quality factor

• Save additional station parameters to output files: source radius, Brune stress drop, source radius, quality factor

• Mark outliers in .out file and in html report

• Colored console output for log messages! (Not supported on Windows)

v1.5: Processing

• New parameter for setting the width of the spectral smoothing window in terms of frequency decades: spectral_smooth_width_decades (see issue #2)

• Compute spectral weights after spectral correction (when a station residuals file is specified via residuals_filepath)

• Removed configuration parameter trace_format

• New configuration parameter sensitivity to provide a constant sensor sensitivity (flat response curve), which overrides any response curve provided in metadata

• New parameter for manually specifying trace units: trace_units (defaults to auto)

• New approach for trace clipping detection (requires just one configuration parameter, named clip_max_percent)

  • Check for trace clipping only in the processing window

  • Use histogram of samples to detect clipping

• Fix for wrong component used for ‘SV’ spectra (see issue #3)

v1.5: Inversion

• New config option: Mw_0_variability. Allowed variability around Mw_0 during the main inversion. Previously hardcoded to 0.1

• New inversion methods for grid sampling:

  • grid search (very slow!)

  • importance sampling of the misfit grid using k-d tree (faster, but less accurate)

• Fix for Basin-hopping algorithm not running

v1.5: Post-Inversion

• New set of post-inversion parameters to reject certain inversion results, per-station: pi_fc_min_max, pi_t_star_min_max, pi_bsd_min_max, pi_misfit_max

• Reject inversion results when inverted fc is within 10% of fc_min or fc_max

• Fix: use logarithmic error width for weighted logarithmic averages

  • previous way of computing weighted logarithmic averages was not correct!

• Option to reject outliers using the IQR (interquartile range) method: parameter nIQR

• Support for non symmetric error on station spectral parameters

• Compute additional, per-station parameters: source radius, Brune stress drop and quality factor

• Compute errors for all station parameters

• Compute weighted averages for all event parameters (except radiated energy)

• Compute spectral residuals using weighted average spectral parameters

v1.5: Plotting

• Source parameter box plots to evaluate parameter dispersion across stations and visually detect outliers

• Misfit plot (2D and 1D) when using grid sampling

• cartopy removed as installation dependency, since it is not easily installable via pip

• Use GSHHS database to draw coastlines.

  • New config option: plot_coastline_resolution

• Correctly show circles on maps with diagonal smaller than 1 km

• Fix plotting map colorbar on Matplotlib 3.5

• Make average and errorbar lines more visible on map colorbar

• Fix for error on plotting fc map, when only one station is available

• Fix trace plot scaling for traces with larger signal outside the plot window

• Do not plot ‘H’ spectrum if there is only one instrument component (since it will coincide with the only component)

• Plot uncorrected spectrum when station correction is used

v1.4 - 2021-10-13

• New config option rps_from_focal_mechanism to compute station-specific S-wave radiation pattern from focal mechanism, if a focal mechanism is available in the QuakeML file

• Plot the focal mechanism on maps, if it is available

• Change default inversion algorithm to TNC (truncated Newton algorithm)

• Config option dataless renamed to station_metadata

• Config option traceids renamed to traceid_mapping_file

• Config options ignore_stations and use_stations renamed to ignore_traceids and use_traceids, respectively

• Support for 2D NonLinLoc grids (via nllgrid >= 1.4.1)

• Possibility of using a generic DEFAULT NonLinLoc time grid

• Added cartopy as an installation dependency

• Fixed: nllgrid was always requested at runtime

• Fixed: gracefully handle the case when there is no internet connection and map tiles cannot be downloaded

• Fixed (Windows): suppress colored terminal output, which is not supported

• Fixed (Windows): it is now possible to relaunch the same run, without having to delete the output directory first

• Fixed (Windows): use same timezone names than Linux and macOS

v1.3.1 - 2021-09-13

• Fix for HTML report not showing trace and spectral plots

• HTML report: add Corner Frequency in Event Summary

v1.3 - 2021-08-20

• HTML reports

• Option to provide station-specific spectral windowing

v1.2 - 2021-05-20

• Use python-versioneer to manage version numbers

v1.1 - 2021-04-07

• Bug fixes:

  • Accept band code C for broadband seismometers sampled at >=250 Hz

  • Require cartopy>=0.18 for compatibility with matplotlib>=3.3

v1.0 - 2021-03-03

• Simplification of time window parameters:

  • an unique window length, win_length, is used for time-domain S/N ratio calculation and spectral analysis

  • the old parameters, noise_win_length and s_win_length, are no more supported and generate an error

• Reorganized config file and improved inline documentation within the file

  • removed unused option: fc_0

  • removed pre_filt option

  • post-processing check on fc bounds has been removed

• Option to specify non standard instrument codes (e.g., L for acceleration)

• Option to specify filter frequencies for local magnitude computation

• Plotting improvements:

  • Show S/N ratio on trace plots

  • Show spectral S/N ratio on spectrum plots

  • Option to show/hide ignored traces/spectra (low S/N)

• Bug fixes:

  • Pay attention to location code when building average spectra

  • Plotting: avoid overlapping traces with different location code

  • Plotting: avoid overlapping spectra with different location code

v0.9 - 2020-04-24

• Support for QuakeML input and StationXML

• Support for Python 3.5

• Only compatible with ObsPy >= 1.1.0

• Project reorganization:

  • Project renamed to SourceSpec

  • ssp_residuals renamed to source_residuals

  • New installable package (e.g., via pip)

• Spectra are smoothed in log-freq (no more Konno-Ohmachi)

• Inversion is performed in a log-freq space

• Option to invert for t_star_0 on the plateau level

• Traces are filtered before computing S/N ratio

• Trace clipping detection

• Traces are always plotted, even if no inversion is performed

• Use by default a global model for theoretical travel time calculation

• Possibility of using NonLinLoc travel time grids (requires nllgrid)

• New options for P and S arrival time tolerance

• New option for maximum epicentral distance for trace processing

• Possibility of using a NonLinLoc model grid for obtaining vs at the source and at the receiver (requires nllgrid)

• Use log10 of weighting function, since the inversion is done in magnitude units

• Use json format for traceid correction file

• Save config file to output dir (only for source_spec)

• Save run completion time into output file

• Logarithmic average and logarithmic (asymmetric) error bars for Mo, fc and source radius

• Computation of radiated energy

• Station-specific filters

• New parameters: gap_max, overlap_max

• Add legend to spectral plots

• Add event information, code version and run completion time to plots

• Multifigure plotting for traces and spectra (for large number of stations)

• New option to plot a station map, color-coded by station magnitude (requires Cartopy)

• Refactoring of local magnitude computation code:

  • Wood-Anderson amplitude is computed from the whole trace converted to W-A and not converting only the min and max peaks (which is the default in ObsPy)

  • Trace windowing is computed on HF envelopes

  • New option for custom Ml coefficients

  • Remove unnecessary Ml options

• Code cleaning and optimization:

  • Switch from optparse (deprecated) to argparse

  • Code style fixes and refactoring

• New option to update a config file from previous versions

• BUGFIX: Fix a major bug in reading hypo pick file

• BUGFIX: Fix for pick file not read in source_model

v0.8 - 2014-07-11

• Trace plot showing S and noise time windows

• Improved handling of paz files

• Per-station Mo on output file

• Code cleaning and optimization

v0.7 - 2014-04-07

• Code reorganization:

  • inversion code split to its own functions

• Option to use bounded inversion

• Station residuals, through ssp_residuals.py

• source_model can now output tables of trade-off between parameters

• Fix in the way noise trace is computed and processed

• Documentation!

v0.6 - 2013-06-05

• Signal to noise weighting

• Improved local magnitude computation

• New options:

  • time domain integration

  • vertical component

• Largely improved plotting function

• More data formats supported (Antilles, IPOC)

• source_model: a code for plotting theoretical spectra

• Code refactoring

v0.5 - 2013-02-10

• Azimuth computation

• Construction of an overall database

• Local magnitude computation

• konnoOhmachiSmoothing

v0.4 - 2012-04-10

• Logging infrastructure

• Code reorganization

v0.3 - 2012-02-10

• Output is no more printed at screen, but to file

• The plots can be saved to a file as well

• We differentiate between short periods and broad bands

v0.2 - 2012-02-06

Extended and generalized for the CRL application.

v0.1 - 2012-01-17

Initial Python port.

References

[1]

Raul Madariaga. Earthquake scaling laws. In Extreme Environmental Events, pages 364–383. Springer New York, New York, NY, 2011. URL: https://www.researchgate.net/publication/226065848_Earthquake_Scaling_Laws, doi:10.1007/978-1-4419-7695-6_22.

[2]

James N. Brune. Tectonic stress and the spectra of seismic shear waves from earthquakes. Journal of Geophysical Research (1896-1977), 75(26):4997–5009, 1970. doi:10.1029/JB075i026p04997.

[3]

John Boatwright, George L. Choy, and Linda C. Seekins. Regional Estimates of Radiated Seismic Energy. Bulletin of the Seismological Society of America, 92(4):1241–1255, 05 2002. doi:10.1785/0120000932.

[4]

Emile A. Okal. A student's guide to teleseismic body wave amplitudes. Seismological Research Letters, 63(2):169–180, April 1992. doi:10.1785/gssrl.63.2.169.

[5]

Maria Lancieri, Raul Madariaga, and Fabian Bonilla. Spectral scaling of the aftershocks of the Tocopilla 2007 earthquake in northern Chile. Geophysical Journal International, 189(1):469–480, 04 2012. doi:10.1111/j.1365-246X.2011.05327.x.

[6]

Yoshihiro Kaneko and Peter M. Shearer. Seismic source spectra and estimated stress drop derived from cohesive-zone models of circular subshear rupture. Geophysical Journal International, 197(2):1002–1015, March 2014. doi:10.1093/gji/ggu030.

[7]

Raul Madariaga. Dynamics of an expanding circular fault. Bulletin of the Seismological Society of America, 66(3):639–666, June 1976. doi:10.1785/bssa0660030639.

[8]

Tamao Sato and Tomowo Hirasawa. Body wave spectra from propagating shear cracks. Journal of Physics of the Earth, 21(4):415–431, 1973. doi:10.4294/jpe1952.21.415.

[9]

Massimo Di Bona and Antonio Rovelli. Effects of the bandwidth limitation of stress drops estimated from integrals of the ground motion. Bulletin of the Seismological Society of America, 78(5):1818–1825, 10 1988. URL: http://bssa.geoscienceworld.org/cgi/doi/10.1785/BSSA0780051818, doi:10.1785/BSSA0780051818.

[10]

John Boatwright and George L. Choy. Teleseismic estimates of the energy radiated by shallow earthquakes. Journal of Geophysical Research, 91(B2):2095, 1986. doi:10.1029/jb091ib02p02095.

Citing literature

The following is a list, probably incomplete, of journal and conference papers that have used SourceSpec and that I’m aware of. If you have used SourceSpec in a publication and would like to have it listed here, please let me know.

Journal papers

[J1]

C Duverger, S Lambotte, P Bernard, H Lyon-Caen, A Deschamps, and A Nercessian. Dynamics of microseismicity and its relationship with the active structures in the western Corinth Rift (Greece). Geophysical Journal International, 215(1):196–221, June 2018. doi:10.1093/gji/ggy264.

[J2]

M Laporte, L Bollinger, H Lyon-Caen, R Hoste-Colomer, C Duverger, J Letort, M Riesner, B P Koirala, M Bhattarai, T Kandel, C Timsina, and L B Adhikari. Seismicity in far western Nepal reveals flats and ramps along the Main Himalayan Thrust. Geophysical Journal International, 226(3):1747–1763, April 2021. doi:10.1093/gji/ggab159.

[J3]

Frédéric Masson, Samuel Auclair, Didier Bertil, Marc Grunberg, Bruno Hernandez, Sophie Lambotte, Gilles Mazet-Roux, Ludmila Provost, Jean-Marie Saurel, Antoine Schlupp, and Christophe Sira. The Transversal Seismicity Action RESIF: A Tool to Improve the Distribution of the French Seismicity Products. Seismological Research Letters, 92(3):1623–1641, March 2021. doi:10.1785/0220200353.

[J4]

Elif Oral and Claudio Satriano. Future magnitude 7.5 earthquake offshore Martinique: spotlight on the main source features controlling ground motion prediction. Geophysical Journal International, 227(2):1076–1093, June 2021. URL: http://dx.doi.org/10.1093/gji/ggab245, doi:10.1093/gji/ggab245.

[J5]

M. Craiu, A. Craiu, M. Mihai, and A. Marmureanu. An automatic procedure for earthquake analysis using real-time data. Acta Geodaetica et Geophysica, 58(1):1–18, February 2023. doi:10.1007/s40328-023-00402-1.

[J6]

Valentine Lefils, Alexis Rigo, and Efthimios Sokos. MADAM: A temporary seismological survey experiment in Aetolia-Akarnanian region (Western Greece). Bulletin of the Geological Society of Greece, 59(1):158–174, January 2023. doi:10.12681/bgsg.31714.

[J7]

O Lengliné, J Schmittbuhl, K Drif, S Lambotte, M Grunberg, J Kinscher, C Sira, A Schlupp, M Schaming, H Jund, and F Masson. The largest induced earthquakes during the GEOVEN deep geothermal project, Strasbourg, 2018–2022: from source parameters to intensity maps. Geophysical Journal International, 234(3):2445–2457, April 2023. doi:10.1093/gji/ggad255.

[J8]

S. Panebianco, V. Serlenga, C. Satriano, F. Cavalcante, and T. A. Stabile. Semi-automated template matching and machine-learning based analysis of the August 2020 Castelsaraceno microearthquake sequence (southern Italy). Geomatics, Natural Hazards and Risk, May 2023. doi:10.1080/19475705.2023.2207715.

[J9]

T C Sunilkumar, Vineet K Gahalaut, D Srinagesh, and B Naresh. Seismotectonic significance of the December 1, 2020 Haridwar, India earthquake (M 4.3), a lower crust event near the Himalayan topographic front. Journal of Earth System Science, March 2023. doi:10.1007/s12040-023-02072-7.

[J10]

Zibo Wang, Ruifeng Liu, and Wei Liu. Source characteristics of the aftershocks of the Wenchuan and Lushan earthquake sequences in the Longmen-Shan fault zone. Frontiers in Earth Science, January 2023. doi:10.3389/feart.2022.1061754.

[J11]

Pierre Gehl, Pascal Dominique, Hideo Aochi, Mickael Delatre, Jannes Kinscher, and Isabelle Contrucci. Development of an empirical ground-motion model for post-mining induced seismicity near Gardanne, France. Journal of Sustainable Mining, 23(2):98–117, February 2024. doi:10.46873/2300-3960.1408.

Conference papers

[C1]

A. Ade Surya Putra, B. Andri Dian Nugraha, C. Nanang T. Puspito, and D. David P. Sahara. Preliminary result: source parameters for small-moderate earthquakes in Aceh segment, Sumatran fault zone (Northern Sumatra). In 18th Annual Meeting of the Asia Oceania Geosciences Society. WORLD SCIENTIFIC, April 2022. doi:10.1142/9789811260100_0076.

[C2]

Ganzorig Davaasuren and Oyun-Erdene Monkhor. Scaling relations of moment magnitude (Mw) and local magnitude (ML) for large earthquakes in northern Mongolia. 2023. doi:10.13140/RG.2.2.15069.59362.

[C3]

Audrey Chouli, Lucile Costes, David Marsan, Jannes Münchmeyer, Sophie Giffard-Roisin, and Anne Socquet. Search for repeaters in the central part of the Chilean subduction zone. March 2024. doi:10.5194/egusphere-egu24-17042.

[C4]

Marc Grunberg and Sophie Lambotte. A new workflow for revising the seismicity catalog for mainland France, covering the period 2010-2018. March 2024. doi:10.5194/egusphere-egu24-5100.

[C5]

Roberto Cabieces, Thiago C. Junqueira, and Jesús Relinque. SurfQuake (SQ): A new Python toolbox for the workflow process of seismic sources. March 2024. doi:10.5194/egusphere-egu24-2816.

Indices and tables

• Index

• Module Index

• Search Page