@article{Ding2025, 
author = {Qianggang Ding and Zhichao Shen and Weiqiang Zhu and Bang Liu},
title = {DASFormer: self-supervised pretraining for earthquake monitoring},
year = {2025},
journal = {Visual Intelligence},
volume = {3},
pages = {14},
keywords = {Time series forecasting, Self-supervised learning, Earthquake monitoring, Image imputation},
url = {https://www.sciopen.com/article/10.1007/s44267-025-00085-y},
doi = {10.1007/s44267-025-00085-y},
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 like PhaseNet and PhaseNet-2 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.}
}