A physics-based probabilistic forecasting methodology for hazardous microseismicity associated with longwall coal mining
File(s)
Accepted version
Author(s)
Cao, Wenzhuo
Durucan, Sevket
Cai, Wu
Shi, Ji-Quan
Korre, Anna
Type
Journal Article
Abstract
Mining-induced microseismicity is widely considered as a result of slippage of pre-existing critically stressed fractures caused by stress perturbations around an advancing face. An in-depth analysis of the recorded microseismicity associated with longwall top coal caving mining at Coal Mine Velenje in Slovenia has been previously carried out and reported by the authors. It has been concluded that while microseismic event rate is affected by mining intensity (longwall face daily advance rate) as well as local abundance of pre-existing fractures, spatial and magnitude characteristics of microseismicity are predominantly influenced by the latter. Based upon this improved understanding of fracture-slip seismic-generation mechanism, the current work aimed at establishing a data-driven yet physics-based probabilistic forecasting methodology for hazardous microseismicity using microseismic monitoring data with concurrent face advance records. Through performing statistical analyses and probability distribution fitting for temporal, magnitude and spatial characteristics of microseismicity within a time window, a short-term forecasting model is developed to estimate the probability of potentially hazardous microseismicity over the next time interval in the form of a joint probability. The real time forecasting of hazardous microseismicity during longwall coal mining is realised through regularly updating the statistical model using the most recent microseismic sequence datasets and face advance records. This forecasting methodology is featured by the physical basis which provides a good explicability of forecasting results, and the probabilistic perspective which accounts for the stochastic nature of mining-induced microseismicity. This model has been employed to make time-varying forecasts of hazardous microseismicity around two longwall panels over a one-year coal production period at Coal Mine Velenje, and satisfactory results at both panels were achieved. In addition, the analysis suggested that the energy magnitude distribution of microseismicity is a dominant factor in contributing to the potential of hazardous microseismicity. This statistical model using microseismic monitoring data has important implications in the evaluation of mining-induced hazards and optimal control of longwall face advance in burst-prone deep-level mining sites.
Date Issued
2020-12-01
Date Acceptance
2020-10-29
Citation
International Journal of Coal Geology, 2020, 232, pp.1-14
ISSN
0166-5162
Publisher
Elsevier
Start Page
1
End Page
14
Journal / Book Title
International Journal of Coal Geology
Volume
232
Copyright Statement
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Commission of the European Communities
Commission of the European Communities
Identifier
https://www.sciencedirect.com/science/article/pii/S0166516220306236?via%3Dihub
Grant Number
FRCR-CT-2015-00005
RFCR-CT-2010-00002
Subjects
Science & Technology
Technology
Physical Sciences
Energy & Fuels
Geosciences, Multidisciplinary
Geology
Hazardous microseismicity
Seismic hazard assessment
Probabilistic forecasting method
Fracture attributes
Longwall top coal caving mining
0403 Geology
0406 Physical Geography and Environmental Geoscience
0914 Resources Engineering and Extractive Metallurgy
Energy
Publication Status
Published
Date Publish Online
2020-11-02