Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network
Abstract The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temp...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | International Journal of Coal Science & Technology |
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| Online Access: | https://doi.org/10.1007/s40789-025-00759-4 |
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| _version_ | 1850067945339748352 |
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| author | Xu Yang Yapeng Liu Anye Cao Yaoqi Liu Changbin Wang Weiwei Zhao Qiang Niu |
| author_facet | Xu Yang Yapeng Liu Anye Cao Yaoqi Liu Changbin Wang Weiwei Zhao Qiang Niu |
| author_sort | Xu Yang |
| collection | DOAJ |
| description | Abstract The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction, this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network. The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 105 J. In terms of temporal prediction, the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions, and there is no false alarm detected throughout the entire testing period. Moreover, compared to the traditional threshold-based coal burst temporal prediction method, the accuracy of the proposed method is increased by 38.5%. In terms of spatial prediction, the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions, 3 medium hazard predictions, and 4 weak hazard predictions. |
| format | Article |
| id | doaj-art-a2efb1be10574fe7915b6213605ac0cd |
| institution | DOAJ |
| issn | 2095-8293 2198-7823 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Coal Science & Technology |
| spelling | doaj-art-a2efb1be10574fe7915b6213605ac0cd2025-08-20T02:48:11ZengSpringerOpenInternational Journal of Coal Science & Technology2095-82932198-78232025-02-0112111810.1007/s40789-025-00759-4Coal burst spatio-temporal prediction method based on bidirectional long short-term memory networkXu Yang0Yapeng Liu1Anye Cao2Yaoqi Liu3Changbin Wang4Weiwei Zhao5Qiang Niu6School of Computer Science and Technology, China University of Mining and TechnologySchool of Computer Science and Technology, China University of Mining and TechnologySchool of Mines, China University of Mining and TechnologySchool of Mines, China University of Mining and TechnologyState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and TechnologySchool of Mines, China University of Mining and TechnologySchool of Computer Science and Technology, China University of Mining and TechnologyAbstract The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction, this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network. The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 105 J. In terms of temporal prediction, the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions, and there is no false alarm detected throughout the entire testing period. Moreover, compared to the traditional threshold-based coal burst temporal prediction method, the accuracy of the proposed method is increased by 38.5%. In terms of spatial prediction, the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions, 3 medium hazard predictions, and 4 weak hazard predictions.https://doi.org/10.1007/s40789-025-00759-4Coal burstSpatio-temporal predictionMicroseismic spatio-temporal characteristic indicatorsBidirectional long short-term memory network |
| spellingShingle | Xu Yang Yapeng Liu Anye Cao Yaoqi Liu Changbin Wang Weiwei Zhao Qiang Niu Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network International Journal of Coal Science & Technology Coal burst Spatio-temporal prediction Microseismic spatio-temporal characteristic indicators Bidirectional long short-term memory network |
| title | Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network |
| title_full | Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network |
| title_fullStr | Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network |
| title_full_unstemmed | Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network |
| title_short | Coal burst spatio-temporal prediction method based on bidirectional long short-term memory network |
| title_sort | coal burst spatio temporal prediction method based on bidirectional long short term memory network |
| topic | Coal burst Spatio-temporal prediction Microseismic spatio-temporal characteristic indicators Bidirectional long short-term memory network |
| url | https://doi.org/10.1007/s40789-025-00759-4 |
| work_keys_str_mv | AT xuyang coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork AT yapengliu coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork AT anyecao coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork AT yaoqiliu coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork AT changbinwang coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork AT weiweizhao coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork AT qiangniu coalburstspatiotemporalpredictionmethodbasedonbidirectionallongshorttermmemorynetwork |