arXiv (All)
open access
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 la
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Comparative Analysis of Strain-to-Velocity Conversion Methods for Active-Source DAS Data and Collocated Nodal Stations#921664
- A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection#414117
- Detection and Characterization of Seismic Signals with Distributed Acoustic Sensing (DAS)#330163
- Characterizing the Modes of Earth’s Free Oscillations via Submarine Distributed Acoustic Sensing (DAS)#1146069
- E-INSPIRE - II. Finding relics from wide-sky multi-band surveys: A proof-of-concept machine learning regression algorithm#1070095
Record · ID 984472
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive