EffiSeisM: An efficient multi-task deep learning model for earthquake monitoring

Abstract

Earthquake monitoring is essential for providing timely warnings, mitigating disaster impacts, advancing scientific research, and guiding urban planning. Precise seismic waveform analysis enables accurate event detection, magnitude estimation, and a deeper understanding of earthquake mechanisms. In this paper, we propose EffiSeisM, an efficient multi-task deep learning model that combines a state space model with a convolutional architecture for earthquake detection, phase picking, and magnitude estimation. EffiSeisM designed a novel Seismic Scale Conv Module and a Conv-SSM encoder, which effectively capture key seismic features while reducing computational complexity. This design ensures high accuracy and operational efficiency, enabling effective seismic analysis. We evaluate EffiSeisM on the DiTing Dataset and DiTing Dataset 2.0, comprising 3 million seismic samples from China and surrounding regions, and compare its performance with several baseline models. The results show that EffiSeisM consistently outperforms the baselines, achieving F1 scores of 0.98 for earthquake detection, 0.92 for phase-P picking, 0.84 for phase-S picking, and an R${}^2$ of 0.92 for magnitude estimation. Additionally, EffiSeisM demonstrates significant improvements in inference speed and accuracy, highlighting its potential as a scalable and efficient solution for large-scale seismic data analysis.