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

Sungil Kim, Byungjoon Yoon, Jung-Tek Lim and Myungsun Kim
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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

Energies, 2021, vol. 14, issue 5, 1-20

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.

Keywords: Pohang; microseismic data; STA/LTA triggering; supervised learning; unsupervised learning; signal–noise classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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