论文标题
使用深度学习从光纤充电DAS数据中检测和表征微观事件
Detection and characterization of microseismic events from fiber-optic DAS data using deep learning
论文作者
论文摘要
微作用分析是地球地下裂缝表征的宝贵工具。由于分布式的声感应（DAS）纤维在井中的深度部署，因此它们具有高分辨率微作用分析的巨大潜力。但是，在连续DAS数据中，准确检测微作用信号是具有挑战性且耗时的。我们设计，训练和部署一个深度学习模型，以自动检测DAS数据中的微震函事件。我们创建了一个策划的数据集，其中包括近7,000个手动选择事件和相等数量的背景噪声示例。我们通过贝叶斯优化优化了深度学习模型的网络体系结构以及其训练超参数。训练有素的模型在我们的基准数据集上达到了98.6％的精度，甚至发现手动标签期间错过的低振幅事件。我们的方法论检测到100,000多个事件，允许重建时空骨折发育比传统方法可行的更准确，更有效。
Microseismic analysis is a valuable tool for fracture characterization in the earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, they hold vast potential for high-resolution microseismic analysis. However, the accurate detection of microseismic signals in continuous DAS data is challenging and time-consuming. We design, train, and deploy a deep learning model to detect microseismic events in DAS data automatically. We create a curated dataset of nearly 7,000 manually-selected events and an equal number of background noise examples. We optimize the deep learning model's network architecture together with its training hyperparameters by Bayesian optimization. The trained model achieves an accuracy of 98.6% on our benchmark dataset and even detects low-amplitude events missed during manual labeling. Our methodology detects more than 100,000 events allowing the reconstruction of spatio-temporal fracture development far more accurately and efficiently than would have been feasible by traditional methods.
