This package contains a least trimmed squares algorithm modified for geophysical array processing and written in Python3. An extensive module of helper functions is also included. These codes are referenced in > Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). Improved infrasound array processing with robust estimators, Geophys. J. Int., 221 p. 2058-2074 doi: https://doi.org/10.1093/gji/ggaa110.
A broader set of geophysical array processing codes are available here, which utilizes this package as the default (and preferred) array processing algorithm.
Infrasonic and seismic array processing often relies on the plane wave assumption. With this assumption, inter-element travel times can be regressed over station (co-)array coordinates to determine an optimal back-azimuth and velocity for waves crossing the array. Station errors such as digitizer timing issues, reversed polarity, and flat channels can manifest as apparent deviations from the plane wave assumption as travel time outliers. Additionally, physical deviations from the plane wave assumption also appear as travel time outliers. This project identifies these outliers from infrasound (and seismic) arrays through the least trimmed squares robust regression technique. Our python implementation uses the FAST_LTS algorithm of Rousseeuw and Van Driessen (2006). Please see Bishop et al. (2020) for processing examples at arrays from the International Monitoring System and Alaska Volcano Observatory.
We recommend using conda and creating a new conda environment such as:
conda create -n py_array -c conda-forge python=3 obspy
Information on conda environments (and more) is available here.
After setting up the conda environment, install the package by running the terminal commands:
conda activate py_array git clone https://github.com/uafgeotools/lts_array cd lts_array pip install -e .
The final command installs the package in “editable” mode, which means
that you can update it with a simple git pull in your local
repository. This install command only needs to be run once.
and their dependencies.
To access the functions in this package, use the following line (for example):
>> python import lts_array as lts_array
See the included Example_Processing.py.
If you use this code for array processing, we ask that you cite the following papers:
- Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). Improved infrasound array processing with robust estimators, Geophys. J. Int., Vol(num) p. ###-### doi: https://doi.org/10.1093/gji/ggaa110
- Rousseeuw, P. J. & Van Driessen, K., 2006. Computing LTS regression for large data sets, Data Mining and Knowledge Discovery, 12(1), 29-45 doi: https://doi.org/10.1007/s10618-005-0024-4
MIT (c)
This work was made possible through support provided by the Defense Threat Reduction Agency Nuclear Arms Control Technology program under contract HDTRA1-17-C-0031. Distribution Statement A: Approved for public release; distribution is unlimited.