{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T11:39:57Z","timestamp":1774179597570,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T00:00:00Z","timestamp":1633996800000},"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":["41807255"],"award-info":[{"award-number":["41807255"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Re-search Project","award":["SKLGP2018Z016\uff0cSKLGP2017Z001"],"award-info":[{"award-number":["SKLGP2018Z016\uff0cSKLGP2017Z001"]}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2019YJ0465"],"award-info":[{"award-number":["2019YJ0465"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw \u2265 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes.<\/jats:p>","DOI":"10.3390\/s21206762","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T06:38:41Z","timestamp":1634107121000},"page":"6762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8412-7188","authenticated-orcid":false,"given":"Hang","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Chongqing City Construction Investment (Group) Co., Ltd., Chongqing 400023, China"}]},{"given":"Jun","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-8022","authenticated-orcid":false,"given":"Chunchi","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Tianbin","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yelin","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1016\/j.ijrmms.2011.06.009","article-title":"Microseismic monitoring and stability analysis of the left bank slope in Jinping first stage hydropower station in southwestern China","volume":"48","author":"Xu","year":"2011","journal-title":"Int. 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