A pre-seismic anomaly detection approach based on graph attention isomorphism network

Pre-seismic anomaly detection plays a crucial role in reducing economic losses and casualties caused by earthquakes. This paper proposes a novel four-step approach for pre-seismic anomaly detection. In the first step, a series of pre-seismic features are extracted by analyzing the earthquake catalog and geomagnetic signals. In the second step, the multi-view learning strategy is employed to obtain fusion features. In the third step, multiple seismic stations in one seismic zone are treated as a seismic station network, and a pre-seismic anomaly detection model is constructed based on the station network. In the final step, four evaluation indicators are introduced to comprehensively evaluate the detection results. Verification results show that the proposed method is effective and achieves better performance than other existing methods.