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Analysis of Spatiotemporal Characteristics of Microseismic Monitoring Data in Deep Mining Based on ST-DBSCAN Clustering Algorithm

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Název: Analysis of Spatiotemporal Characteristics of Microseismic Monitoring Data in Deep Mining Based on ST-DBSCAN Clustering Algorithm
Autoři: Jingxiao Yu, Hongsen He, Zongquan Liu, Xinzhe He, Fengwei Zhou, Zhihao Song, Dingding Yang
Zdroj: Processes. 13:2359
Informace o vydavateli: MDPI AG, 2025.
Rok vydání: 2025
Popis: Analyzing the spatiotemporal characteristics of microseismic monitoring data is crucial for the monitoring and early prediction of coal–rock dynamic disasters during deep mining. Aiming to address the challenges hampering the early prediction of coal–rock dynamic disasters in deep mining, in this paper, we propose a method for analyzing the spatiotemporal characteristics of microseismic events in deep mining based on the ST-DBSCAN algorithm. First, a spatiotemporal distance metric model integrating temporal and spatial distances was constructed to accurately describe the correlations between microseismic events in spatiotemporal dimensions. Second, along with the spatiotemporal distribution characteristics of microseismic data, we determined the spatiotemporal neighborhood parameters suitable for deep-mining environments. Finally, we conducted clustering analysis of 14 sets of actual microseismic monitoring data from the Xinjulong Coal Mine. The results demonstrate the precise identification of two characteristic clusters, namely middle-layer mining disturbances and deep-seated activities, along with isolated high-magnitude events posing significant risks.
Druh dokumentu: Article
Jazyk: English
ISSN: 2227-9717
DOI: 10.3390/pr13082359
Rights: CC BY
Přístupové číslo: edsair.doi...........faa5d2a810dfb55abaf6e80969f7e9bf
Databáze: OpenAIRE
Popis
Abstrakt:Analyzing the spatiotemporal characteristics of microseismic monitoring data is crucial for the monitoring and early prediction of coal–rock dynamic disasters during deep mining. Aiming to address the challenges hampering the early prediction of coal–rock dynamic disasters in deep mining, in this paper, we propose a method for analyzing the spatiotemporal characteristics of microseismic events in deep mining based on the ST-DBSCAN algorithm. First, a spatiotemporal distance metric model integrating temporal and spatial distances was constructed to accurately describe the correlations between microseismic events in spatiotemporal dimensions. Second, along with the spatiotemporal distribution characteristics of microseismic data, we determined the spatiotemporal neighborhood parameters suitable for deep-mining environments. Finally, we conducted clustering analysis of 14 sets of actual microseismic monitoring data from the Xinjulong Coal Mine. The results demonstrate the precise identification of two characteristic clusters, namely middle-layer mining disturbances and deep-seated activities, along with isolated high-magnitude events posing significant risks.
ISSN:22279717
DOI:10.3390/pr13082359