LAUSR

Deep learning has been applied to microseismic event detection over the past few years. However, it is still challenging to detect microseismic events from records with low signal-to-noise ratios (SNRs). To achieve high accuracy of event detection in low-SNR scenario, we propose an end-to-end network that jointly performs denoising and classification tasks (JointNet), and apply it to fiber-optic distributed acoustic sensing (DAS) microseismic data. The JointNet consists of 2D convolution layers that are suitable for extracting features (such as moveout and amplitude) of the dense DAS data. Moreover, the JointNet uses a joint loss, rather than any intermediate loss, to simultaneously update the coupled denoising and classification modules. With the above advantages, the JointNet is capable of simultaneously attenuating noise and preserving fine details of the events, and therefore improving the accuracy of event detection. We generate synthetic events and collect real background noise from a real hydraulic fracturing project, and then expand them using data augmentation methods to yield sufficient training datasets. We train and validate the JointNet using training datasets of different SNRs and compare it with the conventional classification networks VGG (Visual Geometry Group) and DVGG (Deep VGG). The results demonstrate the effectiveness of the JointNet: it consistently outperforms the VGG and DVGG in all SNR scenarios; it has a superior capability to detect events, especially in low-SNR scenario. Finally, we apply the JointNet to detect microseismic events from real DAS data acquired during a hydraulic fracturing. The JointNet successfully detects all manually detected events, and has a better performance than VGG and DVGG.