Spatial–Temporal Energy Data Analysis and Elemental Fractal Interpretation of Microseismic Monitoring for Rock Mass Area Failure

The early warning of rock mass failure in deep hard-rock mines presents a significant challenge for mine safety management. Microseismic monitoring data offer a novel analytical approach to address this issue. This study investigates the evolutionary patterns of rock mass failure in mining areas through the analysis of spatiotemporal energy data from microseismic events. Initially, key spatiotemporal energy parameters are extracted to identify microseismic events associated with localized damage and their periodic characteristics. Subsequently, a spatiotemporal fractal dimension analysis method is established to achieve fractal interpretation of the data by integrating field cloud maps. Finally, an early warning model centered on temporal energy is constructed, which delineates warning zones through a comprehensive evaluation of fractal dimensions, thereby providing decision-making support for mine safety.

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Publication Details

Journal
Applied Sciences
Published
2026-02-24
DOI
https://doi.org/10.3390/app16052172
Primary Topic
Rock Mechanics and Modeling
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatial–Temporal Energy Data Analysis and Elemental Fractal Interpretation of Microseismic Monitoring for Rock Mass Area Failure

Yi Liu, Congcong Zhao, Liang Zhao, Naigen Tan
Applied Sciences
Rock Mechanics and Modeling
article

Spatial–Temporal Energy Data Analysis and Elemental Fractal Interpretation of Microseismic Monitoring for Rock Mass Area Failure

Yi Liu, Congcong Zhao, Liang Zhao, Naigen Tan
article en

Abstract

The early warning of rock mass failure in deep hard-rock mines presents a significant challenge for mine safety management. Microseismic monitoring data offer a novel analytical approach to address this issue. This study investigates the evolutionary patterns of rock mass failure in mining areas through the analysis of spatiotemporal energy data from microseismic events. Initially, key spatiotemporal energy parameters are extracted to identify microseismic events associated with localized damage and their periodic characteristics. Subsequently, a spatiotemporal fractal dimension analysis method is established to achieve fractal interpretation of the data by integrating field cloud maps. Finally, an early warning model centered on temporal energy is constructed, which delineates warning zones through a comprehensive evaluation of fractal dimensions, thereby providing decision-making support for mine safety.

Applied SciencesVol. 16(5)
Central South University (CN), China Academy of Safety Sciences and Technology (CN), University of Science and Technology Beijing (CN)
National Natural Science Foundation of China
Peace, Justice and strong institutions
Openalex Percentile: Top 46%
Rock Mechanics and Modeling
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