Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/5/1499 |
| _version_ | 1797542045895622656 |
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| author | Sungil Kim Byungjoon Yoon Jung-Tek Lim Myungsun Kim |
| author_facet | Sungil Kim Byungjoon Yoon Jung-Tek Lim Myungsun Kim |
| author_sort | Sungil Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:25:03Z |
| format | Article |
| id | doaj.art-5cdde8e210954779a6d2a0cdd40031e4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:25:03Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5cdde8e210954779a6d2a0cdd40031e42023-11-21T09:43:32ZengMDPI AGEnergies1996-10732021-03-01145149910.3390/en14051499Data-Driven Signal–Noise Classification for Microseismic Data Using Machine LearningSungil Kim0Byungjoon Yoon1Jung-Tek Lim2Myungsun Kim3Petroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, KoreaPetroleum and Marine Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, KoreaSmartMind, Inc., C-201, 47 Maeheon-ro 8-gil, Seocho-gu, Seoul 06770, KoreaGeologic Environment Research Division, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, KoreaIt 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.https://www.mdpi.com/1996-1073/14/5/1499Pohangmicroseismic dataSTA/LTA triggeringsupervised learningunsupervised learningsignal–noise classification |
| spellingShingle | Sungil Kim Byungjoon Yoon Jung-Tek Lim Myungsun Kim Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning Energies Pohang microseismic data STA/LTA triggering supervised learning unsupervised learning signal–noise classification |
| title | Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning |
| title_full | Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning |
| title_fullStr | Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning |
| title_full_unstemmed | Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning |
| title_short | Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning |
| title_sort | data driven signal noise classification for microseismic data using machine learning |
| topic | Pohang microseismic data STA/LTA triggering supervised learning unsupervised learning signal–noise classification |
| url | https://www.mdpi.com/1996-1073/14/5/1499 |
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