Home > Archive>Volume 95, Issue 6, 2021 >1940-1947. DOI:10.1111/1755-6724.14882
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Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine
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This work is supported by National Key R&D Program of China (Grant No. 2018YFB1501803, 2019YFC1804805-4) and China Geological Survey Project (Grant No. DD2019135).

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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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SUN Feng, HU Haotian, ZHAO Fa, YANG Xinran, CHEN Zubin, WU Haidong, ZHANG Linyou.2021. Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine[J]. Acta Geologica Sinica(),95(6):1940-1947

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History
  • Received:July 28,2021
  • Revised:October 29,2021
  • Online: December 27,2021
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