{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T04:13:46Z","timestamp":1768277626985,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T00:00:00Z","timestamp":1521072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0600706"],"award-info":[{"award-number":["2016YFC0600706"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts\u2019 seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances.<\/jats:p>","DOI":"10.3390\/rs10030461","type":"journal-article","created":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T09:21:37Z","timestamp":1521105697000},"page":"461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment"],"prefix":"10.3390","volume":"10","author":[{"given":"Xueyi","family":"Shang","sequence":"first","affiliation":[{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"}]},{"given":"Xibing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Safety Engineering, Central South University, Changsha 410083, China"}]},{"given":"Antonio","family":"Morales-Esteban","sequence":"additional","affiliation":[{"name":"Department of Building Structures and Geotechnical Engineering, University of Seville, 41004 Sevilla, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0874-1826","authenticated-orcid":false,"given":"Gualberto","family":"Asencio-Cort\u00e9s","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Pablo de Olavide University of Seville, 41013 Sevilla, Spain"}]},{"given":"Zewei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Earthquake Sciences and Engineering, Sysu, Sun Yat-Sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.1016\/j.eswa.2013.01.028","article-title":"\u201cSeismic-mass\u201d density-based algorithm for spatio-temporal clustering","volume":"40","author":"Georgoulas","year":"2013","journal-title":"Expert Syst. 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