Installation has been tested on both OSX and Linux (Ubuntu), and Windows systems. We support Python versions 2.7, 3.4 and 3.5. Note that, although we support Windows, EQcorrscan is optimized for linux style distributions.

Instructions for installing EQcorrscan and the required dependency, openCV are linked from the docs

A note on correlation precision OpenCV computes cross-correlations in the frequency-domain for normal seismic datasets (if the dataset is very small then the cross-correlation will be computed in the time-domain, but this is rare for seismic data). In testing we have found that different methods of installing openCV provide different results for cross-correlations at the very low-end of cross-correlations. We think this comes down to how the ffts are computed. However, for moderate to high cross-correlations (above 0.05 normalised cross-correlation), all methods provide the same result.

The outcome of this is that for very low thresholds, you may see changes in your results, however for standard operations this is not an issue. We have found that differences are, on average, 0.0024 - which shifts the mean of a single channel cross-correlation from very close to zero, to 0.0024, and alters the median. However we have found that this results in no change in the median absolute deviation of the data, so thresholds based on this will be the same, although the cross-correlations themselves will be shifted. You would have to be running a very low threshold to see the result of this (0.5 * MAD, rather than commonly used values around 8 * MAD).

If you want to be kept informed about releases, bug-tracking and enhancements without having to keep looking on github, subscribe to our google group.

The full documentation for this package can be found here: Docs. Any errors including typos and just missing bits can either be fixed by you, or flagged in the issues tab here. We host our docs on readthedocs, which uses sphinx to scrape the docstrings in the codes, so it is simple to match the docs to the codes and change the docstrings.

We also have a github-pages site EQcorrscan, which uses jekyll to build the site. Changes or additions to this site can be made on the gh-pages branch.

This package contains routines to enable the user to conduct matched-filter earthquake detections using obspy bindings when reading and writing seismic data, and the correlation routine in openCV. The OpenCV package is not installed by this software, due to a need to build from source. The user should follow the instructions above for OpenCV install.

We have also added subspace detection and correlation derived pick adjustment.

Also within this package are:

• Clustering routines for seismic data;
• Peak finding algorithm (basic, but appropriate for noisy data);
• Automatic amplitude picker for local magnitude scale;
• Seisan S-file integration for database management and routine earthquake location;
• Obspy.core.event integration, which opens up lots of other functions (Seishub, hypoDDpy etc.);
• Stacking routines including phase-weighted stacking based on Thurber at al. (2014);
• Brightness based template creation based on the work of Frank et al. (2014);
• Singular Value Decomposition derived magnitude calculations based on Rubinstein & Ellsworth (2010).

We are currently hovering around 15,000 lines of code (including doc-strings) - it is probably worth having a look at the docs to check what functions we have. We are writing a series of tutorials included on the EQcorrscan API to highlight key functions.

This package is written by Calum Chamberlain and Chet Hopp of Victoria University of Wellington, and is distributed under the LGPL GNU License, Copyright Calum Chamberlain and Chet Hopp 2015, 2016.

Please fork this project and work on it there then create a pull request to merge back to this main repository. If you are working on a bug-fix then use the develop branch, otherwise, create a feature branch and work on your addition there.

When you make changes please run the tests in the test directory to ensure everything merges with minimum effort. If there is not yet a test to cope with your changes then please write one.

Please document your functions following the other documentation within the functions, these doc-scripts will then be built into the main documentation using Sphinx.

• CJ Chamberlain, DR Shelly, J Townend, TA Stern (2014) Low‐frequency earthquakes reveal punctuated slow slip on the deep extent of the Alpine Fault, New Zealand, G-cubed,doi:10.1002/2014GC005436
• Thurber, C. H., Zeng, X., Thomas, A. M., & Audet, P. (2014). Phase‐Weighted Stacking Applied to Low‐Frequency Earthquakes, BSSA, doi:10.1785/0120140077.
• Frank, W. B., & Shapiro, N. M. (2014). Automatic detection of low-frequency earthquakes (LFEs) based on a beamformed network response, Geophysical Journal International, 197(2), 1215-1223, doi:10.1093/gji/ggu058.
• Rubinstein, J. L., & Ellsworth, W. L. (2010). Precise estimation of repeating earthquake moment: Example from Parkfield, California, BSSA, doi:10.1785/0120100007 fa66e06971b2dd1e4bf766