SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing
Fibre optic sensing, such as distributed acoustic sensing (DAS), has become a widespread technology for geophysical studies. To process the large-scale datasets produced by DAS, several machine learning methods have been proposed. However, without standardization of data and models, these methods lack comparability and interoperability. This introduces a gap between model developers and practitioners analyzing DAS data and inhibits adoption of deep learning for DAS. To address these limitations, here we present SeisBench DAS, an extension to the SeisBench library for machine learning in seismology. SeisBench DAS defines standard formats for DAS benchmark datasets, including standardised m...
| Authors | Jannes Münchmeyer, Han Xiao, Frederik Tilmann |
|---|---|
| Primary category | physics.geo-ph |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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