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.
Highlights
For the convolutional neural network (CNN) model case, we found that the overall amplitude fluctuation pattern of the vertical component from the noise samples is similar except for that of sample number 19, which has an abrupt elevation in amplitude at the end of the data points (Figure 13d)
This study validated the potential of successful machine learning applicability for signal–noise classification of microseismic data from the Pohang enhanced geothermal system (EGS) project
The seismic data presented in this study are the first and unique microseismic data obtained during the hydraulic-stimulation test for the first EGS project in Korea
Summary
Any signs of collapses or earthquakes should be dealt with appropriately as mine collapses are bound to cause casualties [7,8]
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