期刊
SENSORS
卷 21, 期 19, 页码 -出版社
MDPI
DOI: 10.3390/s21196627
关键词
distributed acoustic sensors; microseismic monitoring; neural networks
资金
- Ministry of Science and Higher Education of the Russian Federation [075-10-2020-119]
Fiber-optic cables have become popular for microseismic monitoring due to their robustness and high resolution, and a novel deep learning approach has been proposed to process large amounts of DAS data in real time. The trained network showed capability in detecting and locating microseismic events, updating velocity models with high precision, and reducing human expert data handling.
Fiber-optic cables have recently gained popularity for use as Distributed Acoustic Sensing (DAS) arrays for borehole microseismic monitoring due to their physical robustness as well as high spatial and temporal resolutions. As a result, the sensors record large amounts of data, making it very difficult to process in real-/semi-real-time using the conventional processing routines. We present a novel approach, based on deep learning, for handling the large amounts of DAS data in real-/semi-real-time. The proposed neural network was trained on synthetic microseismic data contaminated with real-ambient noise from field data and was validated using field DAS microseismic data obtained from a hydraulic fracturing operation. The results indicate that the trained network is capable of detecting and locating microseismic events from DAS data and simultaneously update the velocity model to a high degree of precision. The mean absolute errors in the event locations and the velocity model parameters are 2.04, 0.72, 2.76, 4.19 and 0.97 percent for distance (x), depth (z), P-wave velocity, S-wave velocity and density, respectively. In addition to automation and computational efficiency, deep learning reduces human expert data handling during processing, thus preserving data integrity leading to more accurate and reproducible results.
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