SeisT: A foundational deep learning model for earthquake monitoring tasks

1 code implementation • 2 Oct 2023 • Sen Li, Xu Yang, Anye Cao, Changbin Wang, Yaoqi Liu, Yapeng Liu, Qiang Niu

The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1. 7%, 9. 5%, and 8. 0%, respectively.

Out-of-Distribution Generalization