Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
| Autor: | Jungtek Lim, Myungsun Kim, Sungil Kim, Byungjoon Yoon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Control and Optimization
010504 meteorology & atmospheric sciences Computer science Energy Engineering and Power Technology STA/LTA triggering 010502 geochemistry & geophysics Machine learning computer.software_genre unsupervised learning 01 natural sciences Convolutional neural network supervised learning lcsh:Technology Data-driven Electrical and Electronic Engineering Cluster analysis Engineering (miscellaneous) 0105 earth and related environmental sciences microseismic data Renewable Energy Sustainability and the Environment business.industry lcsh:T Supervised learning Building and Construction Automation Random forest ComputingMethodologies_PATTERNRECOGNITION signal–noise classification Unsupervised learning Artificial intelligence business computer Pohang Energy (miscellaneous) Test data |
| Zdroj: | Energies, Vol 14, Iss 1499, p 1499 (2021) Energies; Volume 14; Issue 5; Pages: 1499 |
| ISSN: | 1996-1073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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