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Published June 30, 2020 | Submitted
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Automated Seismic Source Characterisation Using Deep Graph Neural Networks

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.

Additional Information

License: CC-By Attribution 4.0 International. Submitted: May 24, 2020; Last edited: June 29, 2020. We thank the Associate Editor and two anonymous reviewers for their thoughtful comments on the manuscript. MvdE is supported by French government through the UCAJEDI Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01. The authors acknowledge computational resources provided by the ANR JCJC E-POST project (ANR-14-CE03-0002-01JCJC E-POST). Python codes and the pre-trained model are available from: https://doi.org/10.6084/m9.figshare.12231077. Author asserted no Conflict of Interest.

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August 22, 2023
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October 23, 2023