A dual branch model for predicting microseismic magnitude time series named DTFNet
Abstract Microseismic monitoring is crucial in realizing intelligent early warning of coal mine rockbursts. Utilizing historical microseismic monitoring data to predict future microseismic events effectively enhances the accuracy of impact disaster prediction and early warning. Due to the complexity...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-93272-2 |
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| Summary: | Abstract Microseismic monitoring is crucial in realizing intelligent early warning of coal mine rockbursts. Utilizing historical microseismic monitoring data to predict future microseismic events effectively enhances the accuracy of impact disaster prediction and early warning. Due to the complexity and nonlinearity of microseismic data, conventional time series prediction models struggle to forecast them accurately. Therefore, this paper proposes a microseismic time series prediction model, DTFNet, which integrates time series decomposition and deep learning. Initially, the original microseismic magnitude data is decomposed, reconstructed, and subjected to secondary decomposition using complementary ensemble empirical mode decomposition, permutation entropy, and variational mode decomposition. Subsequently, a dual branch time series prediction model is constructed, which effectively models the microseismic time series data and deeply extracts the features contained in the microseismic magnitude data. This paper uses microseismic monitoring catalogs from multiple working faces as the dataset to predict microseismic magnitudes. The model’s performance is evaluated using four metrics: mean squared error, mean absolute error, relative standard error, and root mean squared error. Experiments show that the proposed method effectively predicts the trend of microseismic magnitude changes and demonstrates good generalization and accuracy. Compared to several popular deep learning time series prediction models, DTFNet reduces the four evaluation metrics by an average of 23%, 18.1%, 11.1%, and 12%, respectively, showcasing a significant competitive advantage. |
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| ISSN: | 2045-2322 |