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        "author": "Benjamin Boland",
        "author_email": "bolandb@student.unimelb.edu.au",
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        "classifiers": [
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering"
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        "description": "SeisSuite\n========================\nThis project is dedicated to provide a Python framework for seismic noise tomography, \nbased on [ObsPy](https://github.com/obspy/obspy/wiki) and numerical Python packages \nsuch as [numpy](http://www.numpy.org/) and [scipy](http://www.scipy.org/).\n\nRequirements\n------------\nThe code is developped and tested on Ubuntu (but should run on other platforms as well)\nwith Python 2.7.\n\nIn addition to [Python 2.7](https://www.python.org/download/releases/2.7/), you need\nto install the following packages:\n\n- [numpy](http://www.numpy.org/) >= 1.8.2\n- [scipy](http://www.scipy.org/) >= 0.13.3\n- [matplotlib](http://matplotlib.org/) >= 1.3.1\n- [ObsPy](https://github.com/obspy/obspy/wiki) >= 0.9.2\n- [pyshp](https://github.com/GeospatialPython/pyshp)\n- [pyproj](https://code.google.com/p/pyproj/) >= 1.8.9\n- [pyPdf](http://pybrary.net/pyPdf/)\n\n\nIt is recommended to install these packages with `pip install ...` or with your\nfavourite package manager, e.g., `apt-get install ...`.\n\nOptionally, you may want to install: \n- [Computer Programs in Seismology](http://www.eas.slu.edu/eqc/eqccps.html) \n  to be able to invert your dispersion maps for a 1-D shear velocity model,\n  as these programs take care of the forward modelling.\n\n- [waveloc](https://github.com/amaggi/waveloc)\n  to be able to run the kurtosis and migration-based event detector and locator,\n  this would enable for an automated removal of earthquake events.\n\n- [nonlinloc](http://alomax.free.fr/nlloc/)\n  to be able to run the non-linear event detection algorithms for waveloc\n  and other detection programmes. \n\nHow to start\n------------\nIf you are reading this, then you have either directly downloaded the tar ball or \ncloned this project from github.com/boland1992/SeisSuite/ \nIn both cases, now you should cd into the SeisSuite directory and run the following\nline in the terminal:\n\n>>> python setup.py install \n\nThis should successfully install all of the module package files required for seissuite. \nIf you wish to check for a successful installation, run this line in any python shell \nthat is correctly linked to your PYTHONPATH: \n\n>>> import seissuite\n\nIf no errors occur, then the installation has been successful. \n\nNext, you should start reading the example configuration file contained in:\n\n./bin/config_example.cnf \n\nwhich contains global parameters and detailed instructions. You should then create \nyour own configuration file (any name with cnf extension, \\*.cnf) with your\nown parameters, and place it in the same folder as the scripts. It is not advised\nto simply modify `./bin/config_example.cnf`, as any update may revert your changes.\n\nYou may then process in recommended order (items and tools from the seissuite module can\nbe used independently of these scripts to create your own application if necessary):\n\n- `00_setup.py` sets up the initial required file structure for the applications.\n\nAFTER THE FILE STRUCTURE HAS BEEN INITIALISED IT IS RECOMMENDED THAT YOU THEN PLACE YOUR\nMSEED RAW WAVEFORMS FILES IN THE ./INPUT/DATA FOLDER AND THE ASSOCIATED METADATA IN THE\n./INPUT/XML OR THE ./INPUT/DATALESS FOLDERS. \n\n- `01_database_init.py` sets up the initial databases required for finding files and\ngeneral processing. It requires MSEED files to be in the ./INPUT/DATA folder, and metadata\nto be in either the ./INPUT/XML or the ./INPUT/DATALESS folders. \n\n- `02_timeseries_process.py` takes seismic waveforms as input in order to first\npreprocess the waveforms and then and export cross-correlations between \npairs of stations,\n\n- `03_dispersion_curves.py` takes cross-correlations as input and applies\na frequency-time analysis (FTAN) in order to extract and export group velocity\ndispersion curves,\n\n- `04_tomo_inversion_testparams.py` takes dispersion curves as input and applies\n a tomographic inversion to produce dispersion maps; the inversion parameters\n are systematically varied within user-defined ranges,\n\n- `05_tomo_inversion_2pass.py` takes dispersion curves as input and applies\n a two-pass tomographic inversion to produce dispersion maps: an overdamped\n inversion is performed in the first pass in order to detect and reject outliers\n from the second pass.\n \n- `06_1d_models.py` takes dispersion maps as input and invert them for a 1-D\n  shear velocity model at selected locations, using a Markov chain Monte Carlo\n  method to sample to posterior distribution of the model's parameters.\n \nThe scripts rely on the Python package `pysismo`, which must thus be located\nin a place included in your PATH (or PYTHONPATH) environment variable. The easiest\nchoice is of course to place it in the same folder as the scripts.\n\nHow to update\n-------------\nThe code is still experimental so you should regularly check for (and pull) \nupdates. These will be backward-compatible, **except if new parameters appear \nin the configuration file**.\n\n**In other words, after any update, you should check whether new parameters were added\nto the example configuration file (`tomo_Brazil.cnf`) and insert them accordingly\nto your own configuration file.**\n\nReferences\n----------\nThe cross-correlation procedure of ambient noise between pairs of stations follows\nthe steps advocated by Bensen et al. (2007). \nThe measurement of dispersion curves is based on the frequency-time\nanalysis (FTAN) with phase-matched filtering described in Levshin and Ritzwoller (2001) \nand Bensen et al. (2007).\nThe tomographic inversion implements the linear inversion procedure \nwith norm penalization and spatial smoothing of Barmin et al. (2001).\nThe Markov chain Monte Carlo method is described by Mosegaard and Tarantola (1995), \nand the forward modelling is taken care of by the Computer Programs in Seimology \n(Herrmann, 2013).\n\n- Barmin, M. P., Ritzwoller, M. H. and Levshin, A. L. (2001). \nA fast and reliable method for surface wave tomography. \n*Pure Appl. Geophys.*, **158**, p. 1351\u20131375. doi:10.1007/PL00001225\n\\[[journal](http://link.springer.com/article/10.1007%2FPL00001225)\\]\n\\[[pdf](http://jspc-www.colorado.edu/pubs/2001/1.pdf)\\]\n\n- Bensen, G. D. et al. (2007). Processing seismic ambient noise data to obtain \nreliable broad-band surface wave dispersion measurements. \n*Geophys. J. Int.*, **169**(3), p. 1239\u20131260. doi:10.1111/j.1365-246X.2007.03374.x\n\\[[journal](http://onlinelibrary.wiley.com/doi/10.1111/j.1365-246X.2007.03374.x/abstract)\\]\n\\[[pdf](http://ciei.colorado.edu/pubs/2007/2.pdf)\\]\n\n- Herrmann, R. B., 2013. Computer Programs in Seismology: an evolving tool for \ninstruction and research, *Seismol. Res. Let.*, **84**(6), p. 1081-1088\ndoi: 10.1785/0220110096\n\\[[pdf](http://srl.geoscienceworld.org/content/84/6/1081.full.pdf+html)\\]\n- Levshin, A. L. and Ritzwoller, M. H. (2001). Automated detection, extraction, \nand measurement of regional surface waves. *Pure Appl. Geophys.*, **158**, \np. 1531\u20131545. doi:10.1007/PL00001233\n\\[[journal](http://link.springer.com/chapter/10.1007%2F978-3-0348-8264-4_11)\\]\n\\[[pdf](http://ciei.colorado.edu/pubs/pageoph_01/Levshin_Ritzwoller_pag2001.pdf)\\]\n\n- Mosegaard, K. and Tarantola, A. (1995) Monte Carlo sampling of solutions to inverse\nproblems, *J. Geophys. Res.*, **100**(B7), p. 12431\u201312447\n\\[[journal](http://onlinelibrary.wiley.com/doi/10.1029/94JB03097/abstract)\\]\n\\[[pdf](http://www.ipgp.fr/~tarantola/Files/Professional/Papers_PDF/MonteCarlo_latex.pdf)\\]\n\n- Langet N. et al (2014). Continuous Kurtosis-Based Migration for Seismic Event Detection and Location,\n with Application to Piton de la Fournaise Volcano, La R\u00e9union.\n* Bul. Seis. Soc. Am.*, **104**, p. 229-246. doi:10.1785/0120130107",
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