Joint denoising and classification network: Application to microseismic event detection in hydraulic fracturing distributed acoustic sensing monitoring

Abstract

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 (S/Ns). To achieve high accuracy of event detection in a low-S/N scenario, we have developed an end-to-end network that jointly performs denoising and classification tasks (JointNet) and applied it to fiber-optic distributed acoustic sensing (DAS) microseismic data. JointNet consists of 2D convolution layers that are suitable for extracting features (such as moveout and amplitude) of the dense DAS data. Moreover, JointNet uses a joint loss, rather than any intermediate loss, to simultaneously update the coupled denoising and classification modules. With the preceding advantages, JointNet is capable of simultaneously attenuating noise and preserving fine details of 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 data sets. We train and validate the JointNet using training data sets of different S/Ns and compare it with the conventional classification networks visual geometry group (VGG) and deep VGG (DVGG). The results demonstrate the effectiveness of JointNet: it consistently outperforms the VGG and DVGG in all S/N scenarios and it has a superior capability to detect events, especially in a low-S/N scenario. Finally, we apply JointNet to detect microseismic events from the real DAS data acquired during hydraulic fracturing. JointNet successfully detects all manually detected events and has a better performance than VGG and DVGG.