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INTRODUCTION TO THE EQCORRSCAN PACKAGE

This document is designed to give you an overview of the capabilities and implimentation of the EQcorrscan python module.

1.1 Installation

Most codes should work without any effort on your part. However you must install the packages this package relies on yourself, this includes the follwing packages:

• matplotlib
• numpy
• scipy
• obspy
• joblib
• openCV (2)

This install has only been tested on Linux machines and even then has some issues when installing on 32-Bit versus 64-Bit machines. In this instance you should be prepared for small differences in the results of your correlations relating to foating-point truncation differences between 32 and 64-Bit machines.

If you plan to run the bright_lights.py routines you will need to have NonLinLoc installed on your machine. This is not provided here and should be sourced from NonLinLoc This will provide the Grid2Time routine which is required to set-up a lag-time grid for your velocity model. You should read the NonLinLoc documentation for more information regarding how this process works and the input files you are required to give. You will be using here the ASCII .csv grid files output from grid2time (NonLinLoc) - you may have to enable this in the grid2time source code.

1.2 Functions

This package is divided into sub-directories of core, par and utils. The utils directory contains simple functions for integration with Seisan, these are the Sfile_util.py module and functions therein which are essentially barebones and do not have the full functionality that Seisan can handle. utils also contains a simple peak-finding algorithm find_peaks.py which looks for peaks within noisy data above a certain threshold and within windows.

Within par you will find parameter files which you will need to edit for each of the core scripts. core functions often call on multiple par files so be sure to set them all up. The template_gen_par.py script is used by all core modules and must be set-up. Within this you will define all your template parameters. Currently the templates must all be of the same length, but this may change in a future release.

Within core you will find the core routines to generate templates, (template_gen.py) search for likely templates (bright_lights.py) and compute cross-channel correlations from these templates (match_filter.py).

This package is in the early stages of development and does not have full, reliable functionality as yet - use with caution!