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

TL;DR

This paper introduces a multitask deep learning framework, PhaseNet+, that performs earthquake phase picking, polarity determination, and phase association simultaneously, enhancing earthquake monitoring efficiency.

Contribution

It extends existing phase picking models to a unified multitask framework, enabling comprehensive seismic analysis in an end-to-end manner.

Findings

Improved accuracy in phase picking and association tasks.

Unified model reduces the need for multiple separate algorithms.

Potential for enhanced real-time earthquake monitoring.

Abstract

Seismic waveforms contain rich information about earthquake processes, making effective data analysis crucial for earthquake monitoring, source characterization, and seismic hazard assessment. With rapid developments in deep learning, the state-of-the-art approach in artificial intelligence, many neural network models have been developed to enhance earthquake monitoring tasks, such as earthquake detection, phase picking, and phase association. 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 extend an existing phase picking model, PhaseNet, to create a multitask framework. This extended model, PhaseNet+, simultaneously performs phase arrival-time picking, first-motion polarity determination, and phase association. The outputs from these perception-based models can then be processed by specialized physics-based algorithms to accurately determine earthquake location and focal mechanism. The multitask approach is not limited to the PhaseNet model and can be applied to other state-of-the-art phase picking models, ultimately improving seismic monitoring through a more unified and efficient approach.