Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization.

ABSTRACT: Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph-structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to

SUBMITTER: Zhang X 

PROVIDER: S-EPMC10078111 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature