Automated shear-wave splitting analysis for single- and multi- layer anisotropic media

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.26443/seismica.v2i2.1031. This is version 1 of this Preprint.

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Authors

Thomas Samuel Hudson , Joseph Asplet, Andrew Walker 

Abstract

Shear-wave velocity anisotropy is present throughout the earth. The strength and orientation of anisotropy can be observed by shear-wave splitting (birefringence) accumulated between earthquake sources and receivers. Seismic deployments are getting ever larger, increasing the number of earthquakes detected and the number of source-receiver pairs. Here, we present a new software package, SWSPy, that fully automates shear-wave splitting analysis, useful for large datasets. The software is written in python, so it can be easily integrated into existing workflows. Furthermore, seismic anisotropy studies typically make a single-layer approximation, but in this work we describe a new method for measuring anisotropy for multi-layered media, which is also implemented. We demonstrate the performance of SWSPy for a range of geological settings, from glaciers to Earth's mantle. We show how the package facilitates interpretation of an extensive dataset at a volcano, and how the new multi-layer method performs on synthetic and real-world data. The automated nature of SWSPy and the discrimination of multi-layer anisotropy will improve the quantification of seismic anisotropy, especially for tomographic applications. The method is also relevant for removing anisotropic effects, important for applications including full-waveform inversion and moment magnitude analysis.

DOI

https://doi.org/10.31223/X5R67Z

Subjects

Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics

Keywords

Passive seismology, Seismic anisotropy, python, Software

Dates

Published: 2023-06-07 04:09

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None