{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T22:40:38Z","timestamp":1746398438153,"version":"3.37.3"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20110","51734009"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Department of Science and Technology","award":["2019SDZY02"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Coal burst prediction is an important research hotspot in coal mine production safety. This paper presents FDNet, which is a knowledge and data fusion-driven deep neural network for coal burst prediction. The main idea of FDNet is to extract explicit features based on the existing mine seismic physical model and utilize deep learning to automatically extract the implicit features of mine microseismic data. The key innovations of FDNet include an expert knowledge indicator selection method based on a subset search strategy, a mine microseismic data extraction method based on a deep convolutional neural network, and a feature deep fusion method of mine microseismic data based on an attention mechanism. We conducted a set of engineering experiments in Gaojiapu Coal Mine to evaluate the performance of FDNet. The results show that compared with the state-of-the-art data-driven machines and knowledge-driven methods, the prediction accuracy of FDNet is improved by 5% and 16%, respectively.<\/jats:p>","DOI":"10.3390\/s22083088","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T06:39:31Z","timestamp":1650350371000},"page":"3088","source":"Crossref","is-referenced-by-count":10,"title":["FDNet: Knowledge and Data Fusion-Driven Deep Neural Network for Coal Burst Prediction"],"prefix":"10.3390","volume":"22","author":[{"given":"Anye","family":"Cao","sequence":"first","affiliation":[{"name":"School of Mines, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Jiangsu Engineering Laboratory of Mine Earthquake Monitoring and Prevention, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Xuzhou Wushuo Information Co., Ltd., Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-1523","authenticated-orcid":false,"given":"Yaoqi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mines, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2651-3432","authenticated-orcid":false,"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Yapeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.ijmst.2019.06.009","article-title":"Machine learning methods for rockburst prediction-state-of-the-art review","volume":"29","author":"Pu","year":"2019","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5885","DOI":"10.1007\/s00603-021-02597-7","article-title":"A statistical method to assess the data integrity and reliability of seismic monitoring systems in underground mines","volume":"54","author":"Wang","year":"2021","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_3","first-page":"1","article-title":"Ground motion characteristics and their cumulative impacts to burst risks in underground coal mines","volume":"8","author":"Wang","year":"2022","journal-title":"Geomech. Geophys. Geo-Energy Geo-Resour."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.ijrmms.2015.09.028","article-title":"A principal component analysis\/fuzzy comprehensive evaluation model for coal burst liability assessment","volume":"100","author":"Cai","year":"2016","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ijrmms.2017.01.005","article-title":"Rock burst assessment and prediction by dynamic and static stress analysis based on micro-seismic monitoring","volume":"100","author":"He","year":"2017","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.ijmst.2018.09.002","article-title":"Rockburst mechanism research and its control","volume":"28","author":"He","year":"2018","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.ijrmms.2009.09.003","article-title":"Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions","volume":"47","author":"He","year":"2010","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jsm.2018.07.004","article-title":"Rockburst prediction in kimberlite using decision tree with incomplete data","volume":"17","author":"Pu","year":"2018","journal-title":"J. Sustain. Min."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s11600-018-0178-2","article-title":"Evaluation of burst liability in kimberlite using support vector machine","volume":"66","author":"Pu","year":"2018","journal-title":"Acta Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.ijmst.2018.08.007","article-title":"Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices","volume":"28","author":"Dou","year":"2018","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.ijmst.2017.11.001","article-title":"Rock burst mechanism analysis in an advanced segment of gob-side entry under different dip angles of the seam and prevention technology","volume":"28","author":"Yang","year":"2018","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.proeps.2009.09.085","article-title":"Application of fuzzy neural network in predicting the risk of rock burst","volume":"1","author":"Jian","year":"2009","journal-title":"Procedia Earth Planet. Sci."},{"key":"ref_13","first-page":"343","article-title":"Rockburst prediction using particle swarm optimization algorithm and general regression neural network","volume":"32","author":"Jia","year":"2013","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s000240050100","article-title":"Analysis of high frequency microseismicity recorded at an underground hardrock mine","volume":"150","author":"Butt","year":"1997","journal-title":"Pure Appl. Geophys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3724\/SP.J.1235.2010.00193","article-title":"Preliminary engineering application of microseismic monitoring technique to rockburst prediction in tunneling of Jinping II project","volume":"2","author":"Wang","year":"2010","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_16","first-page":"19","article-title":"Evaluation of rockburst potential in a cut-and-fill mine using energy balance","volume":"8","author":"Wattimena","year":"2012","journal-title":"Int. J. JCRM"},{"key":"ref_17","first-page":"163","article-title":"Correlation of specific energy with rock brittleness concepts on rock cutting","volume":"103","author":"Altindag","year":"2003","journal-title":"J. S. Afr. Inst. Min. Metall."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, R., Lu, X., Li, S., Yang, H., Qiu, J., and Zhang, L. (2020, January 19\u201324). DLEP: A deep learning model for earthquake prediction. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9207621"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1038\/s41586-018-0438-y","article-title":"Deep learning of aftershock patterns following large earthquakes","volume":"560","author":"DeVries","year":"2018","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"149","DOI":"10.14311\/NNW.2018.28.009","article-title":"Large earthquake magnitude prediction in Taiwan based on deep learning neural network","volume":"28","author":"Huang","year":"2018","journal-title":"Neural Netw. World"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.cageo.2014.12.002","article-title":"Detecting precursory patterns to enhance earthquake prediction in Chile","volume":"76","author":"Florido","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.ssci.2011.08.065","article-title":"Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines","volume":"50","author":"Zhou","year":"2012","journal-title":"Saf. Sci."},{"key":"ref_23","first-page":"63","article-title":"Rockburst prediction method based on case reasoning pattern recognition","volume":"25","author":"Su","year":"2008","journal-title":"J. Min. Saf. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.1007\/s11053-020-09755-8","article-title":"Evaluation of rockburst hazard in deep coalmines with large protective island coal pillars","volume":"30","author":"Li","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_25","first-page":"270","article-title":"Application of RBF neural network to rockburst prediction based on rough set theory","volume":"33","author":"Zhang","year":"2012","journal-title":"Rock Soil Mech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s00603-015-0733-y","article-title":"Discrimination of mine seismic events and blasts using the fisher classifier, naive bayesian classifier and logistic regression","volume":"49","author":"Dong","year":"2016","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1038\/289136a0","article-title":"The b-value as an earthquake precursor","volume":"289","author":"Smith","year":"1981","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1785\/BSSA0340040185","article-title":"Frequency of earthquakes in California","volume":"34","author":"Gutenberg","year":"1944","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_30","unstructured":"Terashima, T., and Santo, T. (1977). Quantification of seismicity. Wind and Seismic Effects, Proceedings of the Seventh Joint Panel Conference of the U.S.-Japan Cooperative Program in Natural Resources, Tokyo, Japan, 20\u201323 May 1975, Center for Building Technology Institute for Applied Technology National Bureau of Standards."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"McCann, W., Nishenko, S., Sykes, L., and Krause, J. (1979). Seismic gaps and plate tectonics: Seismic potential for major boundaries. Earthquake Prediction and Seismicity Patterns, Springer.","DOI":"10.1007\/978-3-0348-6430-5_2"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ijmst.2015.11.008","article-title":"Occurrence, predication, and control of coal burst events in the US","volume":"26","author":"Iannacchione","year":"2016","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_33","first-page":"527","article-title":"The principle of entropy and seismological research","volume":"11","author":"Zhu","year":"1988","journal-title":"J. Seismol. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.tust.2018.06.029","article-title":"A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring","volume":"80","author":"Cai","year":"2018","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_35","unstructured":"Aghdam, H.H., and Heravi, E.J. (2017). Guide to Convolutional Neural Networks, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_37","unstructured":"Pinaya, W.H.L., Vieira, S., Garcia-Dias, R., and Mechelli, A. (2020). Convolutional neural networks. Machine Learning, Elsevier."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Guido, G., Haghshenas, S.S., Haghshenas, S.S., Vitale, A., Gallelli, V., and Astarita, V. (2020). Development of a binary classification model to assess safety in transportation systems using GMDH-type neural network algorithm. Sustainability, 12.","DOI":"10.3390\/su12176735"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/TMM.2019.2924576","article-title":"STAT: Spatial-temporal attention mechanism for video captioning","volume":"22","author":"Yan","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","article-title":"Occlusion aware facial expression recognition using CNN with attention mechanism","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, P., Wang, B., and Zhang, L. (2021, January 20\u201325). Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset with Limited Computational Resources. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01311"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.neunet.2021.10.016","article-title":"Structure inference of networked system with the synergy of deep residual network and fully connected layer network","volume":"145","author":"Huang","year":"2022","journal-title":"Neural Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/3088\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T22:35:25Z","timestamp":1736894125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/8\/3088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":43,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22083088"],"URL":"https:\/\/doi.org\/10.3390\/s22083088","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,4,18]]}}}