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Towards End-to-End Earthquake Monitoring Using a Multitask Deep Learning Model

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 in deep learning. However, most current efforts focus on developing separate models for each specific task, leaving the potential of an end-to-end framework relatively unexplored. To address this gap, we expend the PhaseNet model to create a multitask framework. This enhanced model, PhaseNet+, can simultaneously perform talks of phase arrival time picking, first motion polarity determination, and earthquake source parameter prediction. The outputs from these perception-based models can then be processed by specialized physics-based algorithms to accurately determine earthquake location and focal mechanism. Our approach aims to enhance seismic monitoring by adopting a more unified and efficient approach.

Session: Leveraging Cutting-Edge Cyberinfrastructure for Large Scale Data Analysis and Education - I

Type: Oral

Room: Tubughnenq’ 4

Date: 5/2/2024

Presentation Time: 05:30 PM (local time)

Presenting Author: Weiqiang Zhu

Student Presenter: No

Additional Authors