[S-CG40] Science of slow-to-fast earthquakes

[SCG40-01] Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model

★Invited Papers

*Weiqiang Zhu¹ (1.University of California Berkeley)

Advancements in seismic data processing provide crucial insights into earthquake characteristics. Conventional methods used for earthquake monitoring tasks, such as earthquake detection and phase picking, are being enhanced by the rapid advancements of deep learning. However, most of the current research focuses on developing separate models for each specific task, leaving the potential of an end-to-end framework relatively unexplored. To address this gap, we extend the PhaseNet model to introduce a multitask framework. This enhanced model, PhaseNet+, can simultaneously perform tasks of phase arrival time picking, first motion polarity determination, and earthquake source parameter prediction. The outputs of these perception-based models can then be processed by specialized physics-based algorithms to accurately determine earthquake locations and focal mechanisms. Our approach aims to enhance seismic monitoring by adopting a more unified and efficient framework.