Micro‐seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine

Micro‐seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro‐seismic monitoring requires high precision detection of micro‐seismic events with a low signal‐to‐noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine (GRU_SVM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro‐seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine (SVM) as a classifier to improve the micro‐seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short‐term‐average to the long‐term‐average (STA/LTA) method with GRU_SVM method by using hot dry rock micro‐seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro‐seismic events with low signal‐to‐noise ratios. By ignoring smaller micro‐seismic events, the detection procedure can be processed more efficiently, which is able to provide a real‐time observation on the types of hydraulic fracturing in the reservoirs.