lts_array

Introduction

This package contains a least trimmed squares algorithm written in Python3 and modified for geophysical array processing. An extensive collection 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(3) 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.

Motivation

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 method 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). Uncertainty estimates are calculated using the slowness ellipse method of Szuberla and Olson (2004). Please see Bishop et al. (2020) for processing examples at arrays from the International Monitoring System and Alaska Volcano Observatory.

Installation and Usage

See the README for installation and usage instructions.

References and Credits

If you use this code for array processing, we ask that you cite the following papers:

  1. Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). Improved infrasound array processing with robust estimators, Geophys. J. Int., 221(3) p. 2058-2074 doi: https://doi.org/10.1093/gji/ggaa110
  2. 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/s10618-005-0024-43.
  3. Szuberla, C.A.L. & Olson, J.V., 2004. Uncertainties associated with parameter estimation in atmospheric infrasound arrays, J. Acoust. Soc. Am., 115(1), 253–258. doi: https://doi.org/10.1121/1.1635407

License

MIT (c)

Acknowledgements and Distribution Statement

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.