{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:32:00Z","timestamp":1773819120412,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T00:00:00Z","timestamp":1634688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Foundation for Excellent Young Scholars of China","award":["51822407"],"award-info":[{"award-number":["51822407"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51774327 and 51504288"],"award-info":[{"award-number":["51774327 and 51504288"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2282020cxqd055"],"award-info":[{"award-number":["2282020cxqd055"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification.<\/jats:p>","DOI":"10.3390\/s21216967","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"6967","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters"],"prefix":"10.3390","volume":"21","author":[{"given":"Kang","family":"Peng","sequence":"first","affiliation":[{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0908-1009","authenticated-orcid":false,"given":"Longjun","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.ijmst.2021.06.006","article-title":"Implications for rock instability precursors and principal stress direction from rock acoustic experiments","volume":"31","author":"Dong","year":"2021","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.ijrmms.2016.04.021","article-title":"Discriminant models of blasts and seismic events in mine seismology","volume":"86","author":"Dong","year":"2016","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.engfracmech.2018.01.032","article-title":"Collaborative localization method using analytical and iterative solutions for microseismic\/acoustic emission sources in the rockmass structure for underground mining","volume":"210","author":"Dong","year":"2019","journal-title":"Eng. Fract. Mech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.eng.2019.12.016","article-title":"Velocity-Free MS\/AE Source Location Method for Three-Dimensional Hole-Containing Structures","volume":"6","author":"Dong","year":"2020","journal-title":"Engineering"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9732606","DOI":"10.1155\/2019\/9732606","article-title":"Some Developments and New Insights for Microseismic\/Acoustic Emission Source Localization","volume":"2019","author":"Dong","year":"2019","journal-title":"Shock Vib."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dong, L.J., Tong, X.J., and Ma, J. (2021). Quantitative Investigation of Tomographic Effects in Abnormal Regions of Complex Structures. Engineering.","DOI":"10.1016\/j.eng.2020.06.021"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104885","DOI":"10.1016\/j.ijrmms.2021.104885","article-title":"Empty region identification method and experimental verification for the two-dimensional complex structure","volume":"147","author":"Dong","year":"2021","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ijrmms.2018.04.032","article-title":"Discrimination of seismic sources in an underground mine using full waveform inversion","volume":"106","author":"Ma","year":"2018","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1007\/s00603-019-01761-4","article-title":"Focal Mechanism of Mining-Induced Seismicity in Fault Zones: A Case Study of Yongshaba Mine in China","volume":"52","author":"Ma","year":"2019","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.cageo.2012.10.015","article-title":"An integrated methodology for sub-surface fracture characterization using microseismic data: A case study at the NW Geysers","volume":"54","author":"Aminzadeh","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.ijrmms.2019.03.009","article-title":"Ground motions induced by mining seismic events with different focal mechanisms","volume":"116","author":"Ma","year":"2019","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/S1003-6326(13)62487-5","article-title":"Prediction of rockburst classification using Random Forest","volume":"23","author":"Dong","year":"2013","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.ijrmms.2018.07.016","article-title":"Rockburst mechanism and prediction based on microseismic monitoring","volume":"110","author":"Ma","year":"2018","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.tust.2018.06.034","article-title":"Interval non-probabilistic reliability of surrounding jointed rockmass considering microseismic loads in mining tunnels","volume":"81","author":"Dong","year":"2018","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"2468","DOI":"10.1016\/S1003-6326(21)65667-4","article-title":"Influence of temperature on acoustic emission source location accuracy in underground structure","volume":"31","author":"Dong","year":"2021","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ijrmms.2013.04.005","article-title":"Logistic regression and neural network classification of seismic records","volume":"62","author":"Vallejos","year":"2013","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_18","unstructured":"Potvin, Y. (2012). Discrimination of blasts in mine seismology. Proceedings of the Sixth International Seminar on Deep and High Stress Mining, Australian Centre for Geomechanics."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2073","DOI":"10.1785\/0120070215","article-title":"Spectral discrimination between quarry blasts and earthquakes in Southern California","volume":"98","author":"Allmann","year":"2008","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_20","first-page":"519","article-title":"Experimental and empirical research on frequency-spectrum evolvement rule of rockburst precursory microseismic signals of coal-rock","volume":"27","author":"Lu","year":"2008","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1785\/BSSA0550010001","article-title":"Auditory discrimination of seismic signals from earthquakes and explosions","volume":"55","author":"Frantti","year":"1965","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1785\/BSSA0860041042","article-title":"Analysis of high-frequency Pg\/Lg ratios from NTS explosions and western US earthquakes","volume":"86","author":"Taylor","year":"1996","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_23","first-page":"86","article-title":"Application of FFT spectrum analysis to identify microseismic signals","volume":"33","author":"Jiang","year":"2015","journal-title":"Sci. Technol. Rev."},{"key":"ref_24","first-page":"306","article-title":"Recognition of mine microseismic signals based on FSWT time-frequency analysis","volume":"37","author":"Zhao","year":"2015","journal-title":"Chin. J. Geotech. Eng."},{"key":"ref_25","first-page":"41","article-title":"An automatic identification and classification method of complex microseismic signals in mines based on Mel-frequency cepstral coefficients","volume":"14","author":"He","year":"2018","journal-title":"J. Saf. Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/S1464-1895(99)00019-8","article-title":"Seismic events discrimination by neuro-fuzzy merging of signal and catalogue features","volume":"24","author":"Muller","year":"1999","journal-title":"Phys. Chem. Earth Part A Solid Earth Geod."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.cageo.2009.05.006","article-title":"Earthquake-explosion discrimination using genetic algorithm-based boosting approach","volume":"36","author":"Orlic","year":"2010","journal-title":"Comput. Geosci."},{"key":"ref_28","first-page":"777","article-title":"Neural Networks in Seismic Discrimination","volume":"Volume 303","author":"Husebye","year":"1996","journal-title":"Monitoring a Comprehensive Test Ban Treaty"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.soildyn.2017.05.008","article-title":"Improving microseismic event and quarry blast classification using artificial neural networks based on principal component analysis","volume":"99","author":"Shang","year":"2017","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_30","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6967\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:19:28Z","timestamp":1760167168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,20]]},"references-count":30,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21216967"],"URL":"https:\/\/doi.org\/10.3390\/s21216967","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,20]]}}}