BayesISOLA documentation

BayesISOLA is an open-source Python module for solution of seismic source inverse problem. It uses the point source approximation and describes the source in terms of centroid moment tensor.

Copyright: Jiří Vackář
Version: developer’s snapshot 2022-02-20
License: GNU Lesser General Public License, Version 3 (http://www.gnu.org/copyleft/lesser.html)

Method

The used method is described in the following paper: J. Vackář, J. Burjánek, F. Gallovič, J. Zahradník, and J. Clinton (2017). Bayesian ISOLA: new tool for automated centroid moment tensor inversion, Geophys. J. Int., 210(2), 693–705. PDF

Important note

The code is still under development. We would be very happy for your feedback.

Requirements

Anaconda package list of all necessary Python packages

  • NumPy: Fundamental package for scientific computing with Python.

  • matplotlib: Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive.

  • ObsPy: Python framework for processing seismological data.

  • SciPy: Python-based ecosystem of open-source software for mathematics, science, and engineering.

  • pyproj: Python interface to PROJ4 library for cartographic transformations

  • other modules: math, subprocess, shutil, multiprocessing, re, fractions, warnings, os

Installation

  • Download the code from GitHub

  • Install all required packages (recommended via Anaconda, package list)

  • Compile files green/gr_xyz.for and green/elemse.for with a Fortran compiler (tested with ifort and gfortran), the binaries should be at green/gr_xyz and green/elemse, respectively.

  • Run the examples example_2_SAC.py (data saved in files) or example_2_fdsnws.py (data obtained via fdsnws service)

Examples

All necessary inputs for all examples are included in GitHub repository. Just run python example_X.py. The desired output is linked below each example.

Example 2a: SAC files with covariance matrix

Find a centroid moment tensor for Sargans (St. Gallen, Switzerland) Dec 12, 2013 earthquake. Network configuration is described in file network.stn, event information are in event.isl, crustal model in crustal.dat, and waveforms are in form of SAC files.

Download: output directory of example 2a (zip)

Example 2b: fdsnws service with covariance matrix

The same earthquake and configuration as above, just the waveforms and station responses are obtained using fdsnws service.

Download: output directory of example 2b (zip)

Module summary

BayesISOLA.load_data([outdir, logfile, …])

Load all necessary data for MT inversion.

BayesISOLA.grid(data[, location_unc, …])

Creation of space and time grid for grid-searching MT solution.

BayesISOLA.process_data(data, grid[, …])

Process data for MT inversion.

BayesISOLA.covariance_matrix(data)

Design covariance matrix(es).

BayesISOLA.resolve_MT(data, cova[, …])

Class for the solution of the MT inverse problem.

BayesISOLA.plot(solution[, maps, slices, …])

Class for graphic output of MT solution, inverse process, and used data.

BayesISOLA.axitra

Calculate Green’s function using Axitra code.

BayesISOLA.fileformats

Function for reading synthetic waveforms and response files in various fileformats.

BayesISOLA.helpers

Various data manipulation / arithmetic / waveform filtering functions.

BayesISOLA.histogram

Plots histogram (used by BayesISOLA.plot.plot_uncertainty()).

BayesISOLA.inverse_problem

Solves inverse problem in a single grid point for multiple time shifts.

BayesISOLA.MouseTrap

A class for detection of long-period disturbances by their modeling and fitting.

BayesISOLA.MT_comps

Moment tensor decomposition and conversion of its components.

Indices and tables