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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-93272-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849772387649716224 |
|---|---|
| author | Hao Luo Zhongyi Liu Yishan Pan Liang Wang Chao Kong Huan Zhang |
| author_facet | Hao Luo Zhongyi Liu Yishan Pan Liang Wang Chao Kong Huan Zhang |
| author_sort | Hao Luo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b2ec2deb6e6949dda9042a2749c42397 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b2ec2deb6e6949dda9042a2749c423972025-08-20T03:02:21ZengNature PortfolioScientific Reports2045-23222025-03-0115112210.1038/s41598-025-93272-2A dual branch model for predicting microseismic magnitude time series named DTFNetHao Luo0Zhongyi Liu1Yishan Pan2Liang Wang3Chao Kong4Huan Zhang5Faculty of Information, Liaoning UniversityFaculty of Information, Liaoning UniversityFaculty of Information, Liaoning UniversityLiaoning Earthquake AgencyKeyue Technology (Shandong) Group Co., LtdFaculty of Information, Liaoning UniversityAbstract 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.https://doi.org/10.1038/s41598-025-93272-2 |
| spellingShingle | Hao Luo Zhongyi Liu Yishan Pan Liang Wang Chao Kong Huan Zhang A dual branch model for predicting microseismic magnitude time series named DTFNet Scientific Reports |
| title | A dual branch model for predicting microseismic magnitude time series named DTFNet |
| title_full | A dual branch model for predicting microseismic magnitude time series named DTFNet |
| title_fullStr | A dual branch model for predicting microseismic magnitude time series named DTFNet |
| title_full_unstemmed | A dual branch model for predicting microseismic magnitude time series named DTFNet |
| title_short | A dual branch model for predicting microseismic magnitude time series named DTFNet |
| title_sort | dual branch model for predicting microseismic magnitude time series named dtfnet |
| url | https://doi.org/10.1038/s41598-025-93272-2 |
| work_keys_str_mv | AT haoluo adualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT zhongyiliu adualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT yishanpan adualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT liangwang adualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT chaokong adualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT huanzhang adualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT haoluo dualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT zhongyiliu dualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT yishanpan dualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT liangwang dualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT chaokong dualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet AT huanzhang dualbranchmodelforpredictingmicroseismicmagnitudetimeseriesnameddtfnet |