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arXiv 2103.07897physics.geo-ph
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基于多层传感器网络的深度学习低震级地震检测

Deep learning for low-magnitude earthquake detection on a multi-level sensor network

Ahmed Shaheen, Umair bin Waheed, Michael Fehler, Lubos Sokol, Sherif Hanafy

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摘要：

近年来，由于全球诱发地震活动的急剧增加，低震级地震的自动检测已成为一个日益重要的研究课题。低震级震波迹象的检测对于水力压裂、碳捕获和储存以及地热作业的微震监测至关重要，有助于灾害检测和减灾。此外，微地震的检测对于了解更大地震的潜在机制至关重要。多年来，已经提出了各种算法，包括深度学习方法，以检测此类低震级事件。然而，仍然需要改进这些方法在区分局部噪声源和弱震波迹象方面的鲁棒性。在这项研究中，我们提出了一个卷积神经网络（CNN）检测震波迹象从浅钻孔站在荷兰格罗宁根。我们训练一个CNN模型来检测低震级地震，利用格罗宁根G网络的多级传感器配置。每个G网络站包含四个地震检波器，深度分别为50、100、150和200米。与之前仅在单个传感器级别使用三分量地震记录的深度学习方法不同，我们使用整个钻孔的记录作为一个训练示例。这允许我们使用穿过钻孔传感器的能量的时差模式来训练CNN模型，以区分源自地下的事件和来自地面的局部噪声。我们将训练好的CNN模型的预测准确性与STA/LTA和模板匹配算法在两个月的连续记录上进行比较。我们证明了CNN模型在检测目录中缺失的新事件和最小化错误检测方面显示出比STA/LTA和模板匹配更好的性能。

英文摘要：

Automatic detection of low-magnitude earthquakes has become an increasingly important research topic in recent years due to a sharp increase in induced seismicity around the globe. The detection of low-magnitude seismic events is essential for microseismic monitoring of hydraulic fracturing, carbon capture and storage, and geothermal operations for hazard detection and mitigation. Moreover, the detection of micro-earthquakes is crucial to understand the underlying mechanisms of larger earthquakes. Various algorithms, including deep learning methods, have been proposed over the years to detect such low-magnitude events. However, there is still a need for improving the robustness of these methods in discriminating between local sources of noise and weak seismic events. In this study, we propose a convolutional neural network (CNN) to detect seismic events from shallow borehole stations in Groningen, the Netherlands. We train a CNN model to detect low-magnitude earthquakes, harnessing the multi-level sensor configuration of the G-network in Groningen. Each G-network station contains four geophones at depths of 50, 100, 150, and 200 meters. Unlike prior deep learning approaches that use 3-component seismic records only at a single sensor level, we use records from the entire borehole as one training example. This allows us to train the CNN model using moveout patterns of the energy traveling across the borehole sensors to discriminate between events originating in the subsurface and local noise arriving from the surface. We compare the prediction accuracy of our trained CNN model to that of the STA/LTA and template matching algorithms on a two-month continuous record. We demonstrate that the CNN model shows significantly better performance than STA/LTA and template matching in detecting new events missing from the catalog and minimizing false detections.

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