DASFormer: Self-supervised Pretraining for Earthquake Monitoring

Qianggang Ding, Zhichao Shen, Weiqiang Zhu, Bang Liu

Keywords: Distributed Acoustic Sensing, Earthquake Monitoring, Transformer, Self-supervised Pre-training

TL;DR: We present DASFormer, a novel self-supervised pretraining technique on Distributed Acoustic Sensing (DAS) data that models spatial-temporal signal correlation for earthquake monitoring.

Abstract:

Earthquake monitoring is a fundamental task to unravel the underlying physics of earthquakes and mitigate associated hazards for public safety. Distributed acoustic sensing, or DAS, which transforms pre-existing telecommunication cables into ultra-dense seismic networks, offers a cost-effective and scalable solution for next-generation earthquake monitoring. However, current approaches for earthquake monitoring primarily rely on supervised learning, while manually labeled DAS data is quite limited and it is difficult to obtain more annotated datasets. In this paper, we present DASFormer, a novel self-supervised pretraining technique on DAS data with a coarse-to-fine framework that models spatial-temporal signal correlation. We treat earthquake monitoring as an anomaly detection task and demonstrate DASFormer can be directly utilized as a seismic phase detector. Experimental results demonstrate that DASFormer is effective in terms of several evaluation metrics and outperforms state-of-the-art time-series forecasting, anomaly detection, and foundation models on the unsupervised seismic detection task. We also demonstrate the potential of fine-tuning DASFormer to downstream tasks through case studies.

Submission Number: 119