Automated Seismic Source Characterisation Using Deep Graph Neural Networks

This is a Preprint and has not been peer reviewed.

Downloads

Download Preprint

Supplementary Files
Authors

Martijn van den Ende , Jean-Paul Ampuero 

Abstract

Most seismological analysis methods require knowledge of the geographic location of the stations comprising a seismic network. However, common machine learning tools used in seismology do not account for this spatial information, and so there is an underutilised potential for improving the performance of machine learning models. In this work, we propose a Graph Neural Network (GNN) approach that explicitly incorporates and leverages spatial information for the task of seismic source characterisation (specifically, location and magnitude estimation), based on multi-station waveform recordings. Even using a modestly-sized GNN, we achieve model prediction accuracy that outperforms methods that are agnostic to station locations. Moreover, the proposed method is flexible to the number of seismic stations included in the analysis, and is invariant to the order in which the stations are arranged, which opens up new applications in the automation of seismological tasks and in earthquake early warning systems.

DOI

https://doi.org/10.31223/osf.io/nbmzt

Subjects

Artificial Intelligence and Robotics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics

Keywords

graph neural networks, seismic source

Dates

Published: 2020-05-25 02:18

Last Updated: 2020-06-30 06:45

Older Versions
License

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.