Distributed Acoustic Sensing

Also known as DAS

Distributed acoustic sensing (DAS) uses light traveling through fiber-optic cables to measure vibrations continuously along the cable, creating a dense array of virtual sensors. Researchers use signal processing and wave-inversion methods, along with recurrent networks and Transformers, to identify events, estimate subsurface structure, and locate sources. Applications include seismic monitoring, traffic measurement, vessel detection, and whale-song analysis, supported by tools for streaming data, shared datasets, and reusable machine-learning models. A key challenge is making results reliable beyond individual experiments: models must handle changing locations and noise, while evaluations account for missed events, false alarms, latency, and limited labeled data.

Latest papers

11 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning
Sep 23, 2026Hao Tian, Heng Cai, Xiaowei Chen +1Intelligent Transportation Systems Traffic Flow Estimation
Department of Geography, Texas A&M University, College Station, TX 77840, USA · Department of Geology and Geophysics, Texas A&M University, College Station, TX 77840, USA
SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing
Sep 7, 2026Jannes Münchmeyer, Han Xiao, Frederik TilmannDistributed Acoustic Sensing Scientific ML
GFZ Helmholtz Centre for Geosciences · Institute for Geological Sciences, Freie Universität Berlin, Berlin, Germany
An End-to-End Workflow for Fin Whale Song Detection, Note Characterization, and Localization with Distributed Acoustic Sensing
Aug 3, 2026Dídac Diego-Tortosa, Miriam Romagosa, Arantza Ugalde +4Distributed Acoustic Sensing Sound Event Detection
Institut de Ciències del Mar (ICM-CSIC), Barcelona · Institut de Física Corpuscular (IFIC), Centre Mixte Universitat de València (UV-CSIC), Paterna
A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
Jul 30, 2026Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Daniel Pizarro +7Distributed Acoustic Sensing Ship Detection
Universidad de Alcal´a, Departamento de Electr´onica, Alcal´a de Henares, Spain · Institute of Technology, Universidad San Pablo-CEU, CEU Universities, Urbanizaci´on Montepr´incipe, 28668 Boadilla del Monte, Spain · Daza de Vald´es Institute of Optics (IO-CSIC), Madrid, Spain +1
Distributed Acoustic Localization Array Deployed Using a Soft Everting Vine Robot
Jul 22, 2026Sebastian Lorca Godoy, Ciera McFarland, Michael Val +3Direction-of-Arrival Estimation Robotics
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA. · Pontifical Catholic University of Chile, 8320165 Santiago, Región Metropolitana, Chile. · Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA.
Sea-Scan: High-Accuracy, ML-based Dark Vessel Detection and Localisation via Weakly Supervised DAS Monitoring
Jun 19, 2026Tian Tian, Agastya Raj, Lara Flanagan +2Distributed Acoustic Sensing Object Detection
School of Computer Science and Statistics, IRIS Research Group, ADAPT Research Centre, Trinity College Dublin, Ireland · School of Engineering, ADAPT Research Centre, Trinity College Dublin, Ireland
SagnacAssisted Enhanced OTDR for Distributed Acoustic Sensing: A Standardized Benchmark and Engineering Evaluation Framework
Jun 4, 2026Weiguang Wang, Fugen Wu, Hailing Wang +4Distributed Acoustic Sensing Sound Event Detection
East China Jiaotong University,, Nanchang, 330013, Jiangxi, China · School of Materials and Energy, Guangdong University of Technology,, Guangzhou, 510006,, China · Jiangxi Tonghui Technology Group Co., Ltd.,, Nanchang, 330000, Jiangxi, China
DAStatFormer: A Hybrid Multibranch Transformer with Statistical Feature Integration for DAS-Based Pattern Recognitions
May 22, 2026Michel Dione, Jerry Lonlac, Hélène Louis +2Distributed Acoustic Sensing Time Series Transformers
CERI SN - IMT Nord Europe · IMT Nord Europe, Institut Mines-Telecom, Univ. Lille, Centre for Digital Systems Lille, France · IMT Mines Ales, Institut Mines-Telecom, Ales, France
FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing
May 19, 2026Jintao Li, Weichang Li, Kai Tong +1Distributed Acoustic Sensing
State Key Laboratory of Ocean Sensing & Ocean College, Zhejiang University, Zhoushan, 316021, China · College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Radial-Component Predominant-Mode Inversion of Rayleigh Waves: Application to DAS-based Site Characterization
May 16, 2026Mrinal Bhaumik, Brady R. CoxDistributed Acoustic Sensing Inverse Problems
Department of Civil and Environmental Engineering, Utah State University, Logan, Utah, USA
Distributed Acoustic Sensing for Urban Traffic Monitoring: Spatio-Temporal Attention in Recurrent Neural Networks
Mar 14, 2026Izhan Fakhruzi, Manuel Titos, Carmen Benítez +1Spatio-Temporal Attention Distributed Acoustic Sensing
Department of Signal Theory, Telematics and Communications, and the Center for Information and Communication Technologies Research (CITIC), University of Granada, 18014 Granada, Spain

Sep 23, 2026J/K move · Enter open · S save

Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning

Hao Tian, Heng Cai, Xiaowei Chen, Yifan Yang

Department of Geography, Texas A&M University, College Station, TX 77840, USA · Department of Geology and Geophysics, Texas A&M University, College Station, TX 77840, USA

Intelligent Transportation Systems Traffic Flow Estimation Distributed Acoustic Sensing

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Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acoustic sensing (DAS), an innovative seismic observation tool, emerges as a promising solution for real-time urban traffic monitoring at high spatial and temporal scales. Distributed acoustic sensing repurposes existing underground fiber-optic cables as dense, continuous sensor arrays, enabling passive and privacy-preserving monitoring of roadway traffic activity at meter-level spatial and second-level temporal resolution. This study examines whether integrating DAS and deep learning models can serve as a continuous and efficient urban traffic observatory for revealing urban traffic dynamics (i.e. traffic volume and congestion, event-driven changes) at high spatiotemporal resolution. Using a DAS deployment along a roadway network in the City of College Station, Texas, USA, this study develops a deep learning-empowered analytical framework that converts raw ground vibration waveforms into spatiotemporal representations, detects vehicle trajectory, and infers traffic states from aggregated traffic volume and speed. A hybrid training strategy combining synthetic and manually annotated DAS images is used to improve vehicle detection under noisy and congested conditions, with model outputs further aggregated to characterize system-level traffic dynamics.