lpk-py / amb_noise_tools

Python functions to preprocess and correlate ambient-noise data and extract phase-velocity curves

AMBIENT NOISE PROCESSING TOOLS June 2018 This a set of Python functions that can be used to calculate ambient noise cross correlations and to extract phase velocities. Initial Fortran scripts developed by Kees Weemstra (Delft) and modified and extended by Emanuel Kaestle (UPMC Paris, FU Berlin). Please contact Emanuel Kaestle if you have any questions.

This work is documented by two articles which should be cited (at least the more recent one) by anyone who uses this code: Kaestle, E., R. Soomro, C. Weemstra, L. Boschi, and T. Meier, 2016. Two-receiver measurements of phase velocity: cross-validation of ambient-noise and earthquake-based observations. Geophys. J. Int., 207, 1493--1512, doi:10.1093/gji/ggw341. Boschi L., C. Weemstra, J. Verbeke, G. Ekstrom, A. Zunino and D. Giardini, 2013. On measuring surface-wave phase velocity from station-station cross-correlation of ambient signal. Geophys. J. Int., 192, 346--358, DOI: 10.1093/gji/ggs023.

In order to run these scripts you need a recent (as of February 2018) version of Python and the following packages numpy scipy matplotlib obspy I recommend to use the Anaconda python package that can be installed on all types of machines. You can find a pre-packed installer, including obspy, on the Obspy webpage. With Anaconda, missing packages can be installed by typing in a console window conda install package_name

The following functions can be found in noise.py

adapt_timespan : compares two traces and cuts them to an overlapping time range noisecorr : correlates two traces, optional whitening or 1bit normalization velocity_filter : filters cross correlation to show only signals that arrive within a certain range of velocities. get_zero_crossings : extracts zero crossings from a cross correlation trace. Can also be used to get zero crossings from horizontal component CCs (RR,TT). Does not support cross-correlations of non-identical components (RT,ZR,...) extract_phase_velocity : Extracts the phase velocity by picking zero crossings. get_smooth_pv : Extracts the phase velocity by smoothing the zero crossings prior to picking.

You can find a short description and the necessary input parameters for each of these functions in the noise.py script itself.

The folder also includes two example scripts which can be used to check whether everything works as expected (with 4 days of data, not much but enough to get an impression). In example2 the FFT is calculated parallel on several cores to make it faster, however this parallel computing might cause some problems on Windows machines the way it is currently written. Either use a python interpreter such as Spyder or run it from your console by typing: python example1.py for ZZ cross-correlation and phase-velocity extraction. or python example2.py for TT cross-correlation and phase-velocity extraction.

Other scripts: create_ccs.py is a convencience script to create and maintain a sqlite database of daily records (in mseed or SAC format). All records in the database are cross-correlated and the results are saved. Adapt parameters as needed in the header of the script

process_spectra.py is a script to automatically process all cross-correlation spectra that result from create_ccs.py and extract the phase-velocity curves. Also some experimental signal-to-noise ratio filter can be activated to discard measurements that are below a certain threshold. Adapt parameters as needed in the header of the script.

Both scripts can be run with the example data given in the folder.