Abstract: GPS crustal deformation monitoring plays a vital role in the study of earthquake precursors.With the accumulation of observation data,traditional data processing methods face challenges in big data processing.This study proposes an algorithm based on Transformer network and reconstruction error training strategy.This algorithm learns the GPS crustal displacement data when there are no earthquakes by training the Transformer network,outputs normal data,and inputs the reconstruction error of the earthquake GPS crustal displacement data during abnormal times into the Isolation Forest anomaly detection algorithm model to determine whether it is a precursor to earthquake anomalies.We extract two pre-seismic event anomalies with Mw>5 from GPS crustal deformation data,and obtain more comprehensive and common anomaly data phenomena than previous studies.Statistical analysis shows that there are similar anomalies in the GPS crustal deformation data of these observation stations before multiple earthquakes,indicating the existence of similar crustal deformation accumulation and release patterns.These findings underscore the necessity to improve earthquake prediction and prevention by understanding earthquake mechanisms.

Key words: Crustal deformation, Anomaly extraction, Transformer, Global positioning system, Isolation Forest