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.
The Jupyter Notebooks in this repository reproduce the results of van den Ende & Ampuero (2020): "Automated Seismic Source Characterization Using Deep Graph Neural Networks", published in Geophysical Research Letters (doi:10.1029/2020GL088690). These files can also be found on Figshare: https://doi.org/10.6084/m9.figshare.12231077