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Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach
Deep coal-energy mining frequently results in microseismic (MS) events, which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure. Therefore, quantitatively predicting the time, energy, and location (TEL) of future MS events is crucial for understanding and preventing potential catastrophic events. In this study, we introduced the application of spatiotemporal graph convolutional networks (STGCN) to predict the TEL of MS events induced by deep coal-energy mining. Notably, this was the first application of graph convolution networks (GCNs) in the spatiotemporal prediction of MS events. The adjacency matrices of the sensor networks were determined based on the distance between MS sensors, the sensor network graphs we constructed, and GCN was employed to extract the spatiotemporal details of the graphs. The model is simple and versatile. By testing the model with on-site MS monitoring data, our results demonstrated promising efficacy in predicting the TEL of MS events, with the cosine similarity (C) above 0.90 and the mean relative error (MRE) below 0.08. This is critical to improving the safety and operational efficiency of deep coal-energy mining.
Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach
Deep coal-energy mining frequently results in microseismic (MS) events, which may be a precursor to the risk of rockbursts and pose risks to human safety and infrastructure. Therefore, quantitatively predicting the time, energy, and location (TEL) of future MS events is crucial for understanding and preventing potential catastrophic events. In this study, we introduced the application of spatiotemporal graph convolutional networks (STGCN) to predict the TEL of MS events induced by deep coal-energy mining. Notably, this was the first application of graph convolution networks (GCNs) in the spatiotemporal prediction of MS events. The adjacency matrices of the sensor networks were determined based on the distance between MS sensors, the sensor network graphs we constructed, and GCN was employed to extract the spatiotemporal details of the graphs. The model is simple and versatile. By testing the model with on-site MS monitoring data, our results demonstrated promising efficacy in predicting the TEL of MS events, with the cosine similarity (C) above 0.90 and the mean relative error (MRE) below 0.08. This is critical to improving the safety and operational efficiency of deep coal-energy mining.
Prediction of time-energy-location of microseismic events induced by deep coal-energy mining: Deep learning approach
Yue Song (author) / Enyuan Wang (author) / Hengze Yang (author) / Dong Chen (author) / Baolin Li (author) / Yangyang Di (author)
2025
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
| Elsevier | 2025
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| Elsevier | 2024