arXiv AI By Weiguang Wang, Fugen Wu, Hailing Wang, Xuechen Liang, Xiaobin Li, Ru Han, Tianchang Xie

SagnacAssisted Enhanced OTDR for Distributed Acoustic Sensing: A Standardized Benchmark and Engineering Evaluation Framework

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arXiv:2606. 05754v1 Announce Type: cross Abstract: Phase-sensitive optical time-domain reflectometry ($\phi$-OTDR) is widely used in large-scale distributed acoustic sensing (DAS) because it provides distributed spatiotemporal monitoring over long sensing distances.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 2

DAStatFormer: A Hybrid Multibranch Transformer with Statistical Feature Integration for DAS-Based Pattern Recognitions

arXiv:2606. 00081v1 Announce Type: cross Abstract: Distributed Acoustic Sensing (DAS) enables large-scale monitoring through optical fibers, but its high dimensionality and complex spatio-temporal patterns make event classification demanding.

By Michel Dione (CERI SN - IMT Nord Europe), Jerry Lonlac (CERI SN - IMT Nord Europe), H\'el\`ene Louis (CERI SN - IMT Nord Europe), Anthony Fleury (CERI SN - IMT Nord Europe), Stephane Lecoeuche
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Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data

Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise.

arXiv Machine Learning
Jul 31

A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection

arXiv:2607. 28306v1 Announce Type: cross Abstract: Recent incidents of accidental damage and suspected sabotage to submarine telecommunication and power cables, particularly in the Baltic Sea, have underscored their vulnerability and the need for continuous monitoring solutions.

By Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Daniel Pizarro, Javier Tejedor, Sira Elena Palazuelos-Cagigas, Pedro J. Vidal-Moreno, Mar\'ia R. Fern\'andez-Ruiz, Sonia Martin-Lopez, Miguel Gonzalez-Herraez, Roel Vanthillo
arXiv Machine Learning
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SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing

SeisBench DAS is an extension of the SeisBench library that standardizes distributed acoustic sensing (DAS) data, metadata, labels, and models for machine learning. It leverages the xdas framework for data ingestion and virtual array handling, and PyTorch for model application, providing an efficient engine to apply deep learning models across diverse DAS formats. The framework aims to bridge the gap between model developers and practitioners, facilitating the adoption of deep learning in DAS research and allowing easy integration of future developments.

By Jannes M\"unchmeyer, Han Xiao, Frederik Tilmann