{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:20:12Z","timestamp":1775841612086,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:00:00Z","timestamp":1722124800000},"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":["42107182"],"award-info":[{"award-number":["42107182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20224BAB203040"],"award-info":[{"award-number":["20224BAB203040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Jiangxi Provincial Natural Science Foundation Office","doi-asserted-by":"publisher","award":["42107182"],"award-info":[{"award-number":["42107182"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004479","name":"Jiangxi Provincial Natural Science Foundation Office","doi-asserted-by":"publisher","award":["20224BAB203040"],"award-info":[{"award-number":["20224BAB203040"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.<\/jats:p>","DOI":"10.3390\/s24154892","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T12:27:43Z","timestamp":1722256063000},"page":"4892","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1920-6087","authenticated-orcid":false,"given":"Bingyu","family":"Xin","sequence":"first","affiliation":[{"name":"Faculty of Resource and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1086-5886","authenticated-orcid":false,"given":"Zhiyong","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2809-7595","authenticated-orcid":false,"given":"Shijie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6975-2759","authenticated-orcid":false,"given":"Liang","family":"Feng","sequence":"additional","affiliation":[{"name":"Faculty of Resource and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"},{"name":"Jiangxi Provincial Key Laboratory of Environmental Pollution Prevention and Control in Mining and Metallurgy, Ganzhou 341000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1093\/gji\/ggaa287","article-title":"Joint detection and classification of rockfalls in a microseismic monitoring network","volume":"222","author":"Feng","year":"2020","journal-title":"Geophys. 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