A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data
The prediction and classification of rockburst risk based on microseismic data is the premise of preventing rockbursts during deep mine excavation. By reviewing previous studies, this paper finds two problems that hinder the rockburst prediction: 1) there is a lack of research on the distribution fe...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1601090/full |
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| author | Xiufeng Zhang Guoying Li Yang Chen Hao Wang Haikuan Zhang Haitao Li Weisheng Du Xiao Li Xuewei Xu Yuze He |
| author_facet | Xiufeng Zhang Guoying Li Yang Chen Hao Wang Haikuan Zhang Haitao Li Weisheng Du Xiao Li Xuewei Xu Yuze He |
| author_sort | Xiufeng Zhang |
| collection | DOAJ |
| description | The prediction and classification of rockburst risk based on microseismic data is the premise of preventing rockbursts during deep mine excavation. By reviewing previous studies, this paper finds two problems that hinder the rockburst prediction: 1) there is a lack of research on the distribution features of monitoring data on the main controlling factors of rockbursts; 2) there is no research on the intra-class variance and inter-class gap of microseismic data. Based on the typical rockburst risk events, a quantitative information model of geology and mining is constructed. The relationship between the spatial–temporal distribution characteristics of microseismic data before a rockburst and the main controlling factors of a rockburst is studied. The results show that the distribution features may be different for the same type of microseismic (MS) and rockburst events, and different types of events may show similar distribution features. Therefore, based on the quantitative study of the relationship between the performance of a deep learning prediction algorithm and a rockburst prediction vector, a rockburst risk and type prediction algorithm based on a convolutional neural network (CNN)-gated recurrent unit (GRU) model with prototype-based prediction is proposed. The CNN-GRU model can produce prediction vectors by fusing implicit and explicit information extracted from the original MS data and early warning indicators. Cross-entropy loss, vector-prototype contrastive loss, and vector-prototype contrastive loss are proposed to automatically control the intra-class variance and inter-class gap of prediction vectors belonging to different rockburst risks and types. Many experiments show that the performance of the proposed CNN-GRU model with prototype-based prediction is superior to other algorithms in the prediction of rockburst risks and types based on MS data. |
| format | Article |
| id | doaj-art-8907151db42244d084ee00c8f343f829 |
| institution | DOAJ |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-8907151db42244d084ee00c8f343f8292025-08-20T03:21:42ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-05-011310.3389/feart.2025.16010901601090A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic dataXiufeng Zhang0Guoying Li1Yang Chen2Hao Wang3Haikuan Zhang4Haitao Li5Weisheng Du6Xiao Li7Xuewei Xu8Yuze He9Coal Industry Management Department, Shandong Energy Group Co., Ltd., Jinan, Shandong, ChinaCoal Industry Management Department, Shandong Energy Group Co., Ltd., Jinan, Shandong, ChinaCoal Industry Management Department, Shandong Energy Group Co., Ltd., Jinan, Shandong, ChinaCoal Industry Management Department, Shandong Energy Group Co., Ltd., Jinan, Shandong, ChinaDeep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Beijing, ChinaDeep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Beijing, ChinaDeep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Beijing, ChinaDeep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Beijing, ChinaDeep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Beijing, ChinaDeep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Beijing, ChinaThe prediction and classification of rockburst risk based on microseismic data is the premise of preventing rockbursts during deep mine excavation. By reviewing previous studies, this paper finds two problems that hinder the rockburst prediction: 1) there is a lack of research on the distribution features of monitoring data on the main controlling factors of rockbursts; 2) there is no research on the intra-class variance and inter-class gap of microseismic data. Based on the typical rockburst risk events, a quantitative information model of geology and mining is constructed. The relationship between the spatial–temporal distribution characteristics of microseismic data before a rockburst and the main controlling factors of a rockburst is studied. The results show that the distribution features may be different for the same type of microseismic (MS) and rockburst events, and different types of events may show similar distribution features. Therefore, based on the quantitative study of the relationship between the performance of a deep learning prediction algorithm and a rockburst prediction vector, a rockburst risk and type prediction algorithm based on a convolutional neural network (CNN)-gated recurrent unit (GRU) model with prototype-based prediction is proposed. The CNN-GRU model can produce prediction vectors by fusing implicit and explicit information extracted from the original MS data and early warning indicators. Cross-entropy loss, vector-prototype contrastive loss, and vector-prototype contrastive loss are proposed to automatically control the intra-class variance and inter-class gap of prediction vectors belonging to different rockburst risks and types. Many experiments show that the performance of the proposed CNN-GRU model with prototype-based prediction is superior to other algorithms in the prediction of rockburst risks and types based on MS data.https://www.frontiersin.org/articles/10.3389/feart.2025.1601090/fullrockburst predictionrockburst typesdeep learningmicroseismic dataprototype learning |
| spellingShingle | Xiufeng Zhang Guoying Li Yang Chen Hao Wang Haikuan Zhang Haitao Li Weisheng Du Xiao Li Xuewei Xu Yuze He A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data Frontiers in Earth Science rockburst prediction rockburst types deep learning microseismic data prototype learning |
| title | A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data |
| title_full | A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data |
| title_fullStr | A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data |
| title_full_unstemmed | A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data |
| title_short | A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data |
| title_sort | prototype based rockburst types and risk prediction algorithm considering intra class variance and inter class distance of microseismic data |
| topic | rockburst prediction rockburst types deep learning microseismic data prototype learning |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1601090/full |
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