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Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning

Author

Listed:
  • Sungil Kim

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Byungjoon Yoon

    (Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

  • Jung-Tek Lim

    (SmartMind, Inc., C-201, 47 Maeheon-ro 8-gil, Seocho-gu, Seoul 06770, Korea)

  • Myungsun Kim

    (Geologic Environment Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea)

Abstract

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine learning validated that automation based on the proposed supervised and unsupervised learning applications can classify the acquired microseismic data in real time.

Suggested Citation

  • Sungil Kim & Byungjoon Yoon & Jung-Tek Lim & Myungsun Kim, 2021. "Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning," Energies, MDPI, vol. 14(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1499-:d:513460
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    References listed on IDEAS

    as
    1. Kim, Kwang-Il & Min, Ki-Bok & Kim, Kwang-Yeom & Choi, Jai Won & Yoon, Kern-Shin & Yoon, Woon Sang & Yoon, Byungjoon & Lee, Tae Jong & Song, Yoonho, 2018. "Protocol for induced microseismicity in the first enhanced geothermal systems project in Pohang, Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1182-1191.
    2. Byeongcheol Kang & Sungil Kim & Hyungsik Jung & Jonggeun Choe & Kyungbook Lee, 2019. "Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review," Energies, MDPI, vol. 12(10), pages 1-24, May.
    3. Sungil Kim & Kyungbook Lee & Minhui Lee & Taewoong Ahn, 2020. "Data-Driven Three-Phase Saturation Identification from X-ray CT Images with Critical Gas Hydrate Saturation," Energies, MDPI, vol. 13(21), pages 1-19, November.
    4. Sungil Kim & Kyungbook Lee & Minhui Lee & Taewoong Ahn & Jaehyoung Lee & Hwasoo Suk & Fulong Ning, 2020. "Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images," Energies, MDPI, vol. 13(19), pages 1-20, September.
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