CLI Tutorial

NoisePy provides a CLI (command line interface) for running processing jobs. This tutorial works through the same tasks as the Noisepy Colab Tutorial from the get_started.ipynb Jupyter Notebook. The config.yml file contains all parameters and their default value used throughout the tutorial.

Step 0: download data

This step will download data using obspy and save them into ASDF files locally. The data will be stored for each time chunk defined in hours by inc_hours.

The download will clean up the raw data by detrending, removing the mean, bandpassing (broadly), removing the instrumental response, merging gaps, ignoring too-gappy data.

mkdir tmpdata
noisepy download --config config.yml --raw_data_path ./tmpdata/RAW_DATA

Verify the data is there:

ls -la ./tmpdata/RAW_DATA

Notice that this directory contains a config.yml file. This file will contain any parameters used during the download step. It could be different from the original config.yml since all the paramters can be overriden through CLI arguments. See noisepy download --help.

Step 1: Cross-correlation

This step will perform the cross correlation. For each time chunk, it will read the data, perform classic ambient noise pre-processing (time and frequency normalization), FFT, cross correlation, substacking, saving cross correlations in to a temp ASDF file (this is not fast and will be improved).

noisepy cross_correlate --raw_data_path ./tmpdata/RAW_DATA --ccf_path ./tmpdata/CCF

Optionally, this step can be run via MPI (e.g. with 2 processes). See Installation:

mpiexec -n 2 noisepy cross_correlate --mpi --raw_data_path ./tmpdata/RAW_DATA --ccf_path ./tmpdata/CCF

NOTE: We didn’t pass a --config argument explicitly because noisepy will always look for one in the input data directory for the given step, ./tmpdata/RAW_DATA in this case.

Once again, verify the data for this step:

ls -la ./tmpdata/CCF

Step 2: Stack the cross correlation

This combines the time-chunked ASDF files to stack over each time chunk and at each station pair.

noisepy stack --ccf_path ./tmpdata/CCF --stack_path ./tmpdata/STACK

Optionally, this step can be run via MPI (e.g. with 3 processes). See Installation:

mpiexec -n 3 noisepy stack --mpi --ccf_path ./tmpdata/CCF --stack_path ./tmpdata/STACK

ls -R tmpdata/STACK