Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine |
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Authors: | SUN Feng HU Haotian ZHAO F YANG Xinran CHEN Zubin WU Haidong ZHANG Linyou |
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Institution: | College of Instrumentation & Electrical Engineering,Jilin University,Changchun 130026,China;Key Lab of Geo-Exploration Instrumentation of Ministry of Education,Jilin University,Changchun 130026,China;Center for Hydrogeological and Environment Geological Survey,China Geological Survey,Baoding,Hebei 071000,China |
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Abstract: | 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. |
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Keywords: | hot dry rock micro-seismic detection gated recurrent unit support vector machine |
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