The distributed optical fiber sensors (DFOS) are strain, temperature, and vibration monitoring tools characterized by minimal intrusiveness, accuracy, ease of deployment, and the ability to perform measurements with high spatial resolution. Although these sensors rely on well-established. Abstract—In this paper, deep learning models trained with real seismic data are proposed and proven to detect earthquakes in fiber-optic distributed acoustic sensor (DAS) measurements. The proposed neural network architectures cover the three classical deep learning paradigms: fully connected. Distributed Fiber Optic Sensing and the Future of Earthquake Hazards Research: Key Results from USGS Field Experiments Andrew J. McGuire, James Atterholt, Theresa Sawi, Clara Yoon, Morgan P. In particular, Distributed Acoustic Sensing (DAS).
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