lmoresi/seissuite

Python toolkit for ambient noise seismology methods. (combination of existing and newly written codes)

SeisSuite

This project is dedicated to provide a Python framework for seismic noise tomography, based on ObsPy and numerical Python packages such as numpy and scipy.

Requirements

The code is developed and tested on Ubuntu (but should run on other platforms as well) with Python 2.7.

In addition to Python 2.7, you need to install the following packages:

• numpy >= 1.8.2
• scipy >= 0.13.3
• matplotlib >= 1.3.1
• ObsPy >= 0.9.2
• pyshp
• pyproj >= 1.8.9
• pyPdf

It is recommended to install these packages with pip install ... or with your favourite package manager, e.g., apt-get install ....

Optionally, you may want to install:

• Computer Programs in Seismology to be able to invert your dispersion maps for a 1-D shear velocity model, as these programs take care of the forward modelling.

• waveloc to be able to run the kurtosis and migration-based event detector and locator, this would enable for an automated removal of earthquake events.

• nonlinloc to be able to run the non-linear event detection algorithms for waveloc and other detection programmes.

How to start

If you are reading this, then you have either directly downloaded the tar ball or cloned this project from github.com/boland1992/SeisSuite/ In both cases, now you should cd into the SeisSuite directory and run the following line in the terminal:

$ python setup.py install

This should successfully install all of the module package files required for seissuite. If you wish to check for a successful installation, run this line in any python shell that is correctly linked to your PYTHONPATH:

 import seissuite

If no errors occur, then the installation has been successful.

Next, you should start reading the example configuration file contained in:

./bin/config_example.cnf

which contains global parameters and detailed instructions. You should then create your own configuration file (any name with cnf extension, \*.cnf) with your own parameters, and place it in the same folder as the scripts. It is not advised to simply modify ./bin/config_example.cnf, as any update may revert your changes.

You may then process in recommended order (items and tools from the seissuite module can be used independently of these scripts to create your own application if necessary):

• 00_setup.py sets up the initial required file structure for the applications.

AFTER THE FILE STRUCTURE HAS BEEN INITIALISED IT IS RECOMMENDED THAT YOU THEN PLACE YOUR MSEED RAW WAVEFORMS FILES IN THE ./INPUT/DATA FOLDER AND THE ASSOCIATED METADATA IN THE ./INPUT/XML OR THE ./INPUT/DATALESS FOLDERS.

• 01_database_init.py sets up the initial databases required for finding files and general processing. It requires MSEED files to be in the ./INPUT/DATA folder, and metadata to be in either the ./INPUT/XML or the ./INPUT/DATALESS folders.

• 02_timeseries_process.py takes seismic waveforms as input in order to first preprocess the waveforms and then and export cross-correlations between pairs of stations,

• 03_dispersion_curves.py takes cross-correlations as input and applies a frequency-time analysis (FTAN) in order to extract and export group velocity dispersion curves,

• 04_tomo_inversion_testparams.py takes dispersion curves as input and applies a tomographic inversion to produce dispersion maps; the inversion parameters are systematically varied within user-defined ranges,

• 05_tomo_inversion_2pass.py takes dispersion curves as input and applies a two-pass tomographic inversion to produce dispersion maps: an overdamped inversion is performed in the first pass in order to detect and reject outliers from the second pass.

• 06_1d_models.py takes dispersion maps as input and invert them for a 1-D shear velocity model at selected locations, using a Markov chain Monte Carlo method to sample to posterior distribution of the model's parameters.

The scripts rely on the Python package pysismo, which must thus be located in a place included in your PATH (or PYTHONPATH) environment variable. The easiest choice is of course to place it in the same folder as the scripts.

How to update

The code is still experimental so you should regularly check for (and pull) updates. These will be backward-compatible, except if new parameters appear in the configuration file.

In other words, after any update, you should check whether new parameters were added to the example configuration file (tomo_Brazil.cnf) and insert them accordingly to your own configuration file.

References

The cross-correlation procedure of ambient noise between pairs of stations follows the steps advocated by Bensen et al. (2007). The measurement of dispersion curves is based on the frequency-time analysis (FTAN) with phase-matched filtering described in Levshin and Ritzwoller (2001) and Bensen et al. (2007). The tomographic inversion implements the linear inversion procedure with norm penalization and spatial smoothing of Barmin et al. (2001). The Markov chain Monte Carlo method is described by Mosegaard and Tarantola (1995), and the forward modelling is taken care of by the Computer Programs in Seimology (Herrmann, 2013).

• Barmin, M. P., Ritzwoller, M. H. and Levshin, A. L. (2001). A fast and reliable method for surface wave tomography. Pure Appl. Geophys., 158, p. 1351–1375. doi:10.1007/PL00001225 [journal] [pdf]

• Bensen, G. D. et al. (2007). Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements. Geophys. J. Int., 169(3), p. 1239–1260. doi:10.1111/j.1365-246X.2007.03374.x [journal] [pdf]

• Herrmann, R. B., 2013. Computer Programs in Seismology: an evolving tool for instruction and research, Seismol. Res. Let., 84(6), p. 1081-1088 doi: 10.1785/0220110096 [pdf]

• Levshin, A. L. and Ritzwoller, M. H. (2001). Automated detection, extraction, and measurement of regional surface waves. Pure Appl. Geophys., 158, p. 1531–1545. doi:10.1007/PL00001233 [journal] [pdf]

• Mosegaard, K. and Tarantola, A. (1995) Monte Carlo sampling of solutions to inverse problems, J. Geophys. Res., 100(B7), p. 12431–12447 [journal] [pdf]

• Langet N. et al (2014). Continuous Kurtosis-Based Migration for Seismic Event Detection and Location, with Application to Piton de la Fournaise Volcano, La Réunion.

• Bul. Seis. Soc. Am.*, 104, p. 229-246. doi:10.1785/0120130107