DASFormer: self-supervised pretraining 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-effec...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Visual Intelligence |
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| Online Access: | https://doi.org/10.1007/s44267-025-00085-y |
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| _version_ | 1849388828099346432 |
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| author | Qianggang Ding Zhichao Shen Weiqiang Zhu Bang Liu |
| author_facet | Qianggang Ding Zhichao Shen Weiqiang Zhu Bang Liu |
| author_sort | Qianggang Ding |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d26e50fe38fb4e8095c8349fda7b8be7 |
| institution | Kabale University |
| issn | 2097-3330 2731-9008 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Visual Intelligence |
| spelling | doaj-art-d26e50fe38fb4e8095c8349fda7b8be72025-08-20T03:42:09ZengSpringerVisual Intelligence2097-33302731-90082025-07-013112010.1007/s44267-025-00085-yDASFormer: self-supervised pretraining for earthquake monitoringQianggang Ding0Zhichao Shen1Weiqiang Zhu2Bang Liu3Mila - Quebec AI Institute, University of MontrealWoods Hole Oceanographic InstitutionUC Berkeley Seismological LaboratoryMila - Quebec AI Institute, University of MontrealAbstract 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.https://doi.org/10.1007/s44267-025-00085-yEarthquake monitoringImage imputationTime series forecastingSelf-supervised learning |
| spellingShingle | Qianggang Ding Zhichao Shen Weiqiang Zhu Bang Liu DASFormer: self-supervised pretraining for earthquake monitoring Visual Intelligence Earthquake monitoring Image imputation Time series forecasting Self-supervised learning |
| title | DASFormer: self-supervised pretraining for earthquake monitoring |
| title_full | DASFormer: self-supervised pretraining for earthquake monitoring |
| title_fullStr | DASFormer: self-supervised pretraining for earthquake monitoring |
| title_full_unstemmed | DASFormer: self-supervised pretraining for earthquake monitoring |
| title_short | DASFormer: self-supervised pretraining for earthquake monitoring |
| title_sort | dasformer self supervised pretraining for earthquake monitoring |
| topic | Earthquake monitoring Image imputation Time series forecasting Self-supervised learning |
| url | https://doi.org/10.1007/s44267-025-00085-y |
| work_keys_str_mv | AT qianggangding dasformerselfsupervisedpretrainingforearthquakemonitoring AT zhichaoshen dasformerselfsupervisedpretrainingforearthquakemonitoring AT weiqiangzhu dasformerselfsupervisedpretrainingforearthquakemonitoring AT bangliu dasformerselfsupervisedpretrainingforearthquakemonitoring |