Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, 121205 Moscow, Russia.
Department of Exploration Geophysics, Curtin University, 26 Dick Perry Avenue, Kensington, WA 6151, Australia.
Sensors (Basel). 2021 Oct 5;21(19):6627. doi: 10.3390/s21196627.
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 (), depth (), 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.
光纤电缆最近由于其物理强度以及高空间和时间分辨率而在作为分布式声学传感 (DAS) 阵列用于井下微地震监测方面变得越来越受欢迎。结果，传感器记录了大量数据，这使得使用传统处理例程很难实时/半实时地进行处理。我们提出了一种基于深度学习的新方法，用于实时/半实时处理大量 DAS 数据。所提出的神经网络是在受现场数据中真实环境噪声污染的合成微震数据上进行训练的，并使用从水力压裂作业中获得的现场 DAS 微震数据进行了验证。结果表明，经过训练的网络能够从 DAS 数据中检测和定位微震事件，并同时以高精度更新速度模型。事件位置和速度模型参数的平均绝对误差分别为 2.04%、0.72%、2.76%、4.19%和 0.97%，分别为距离()、深度()、P 波速度、S 波速度和密度。除了自动化和计算效率之外，深度学习还减少了处理过程中人工专家数据处理的工作量，从而保持了数据的完整性，从而得到更准确和可重复的结果。
