Rockburst mechanism and prediction based on microseismic monitoring

Ma, Tianhui; Tang, Chun’an; Tang, Shibin; Kuang, Liang; Yu, Qun; Kong, Deqing; Zhu, Xu

doi:10.1016/j.ijrmms.2018.07.016

Cited by 147 publications

(52 citation statements)

References 17 publications

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“…15), indicates that more fractures are deemed to be activated at similar mining conditions and stress environments. This is supported by field observation that most microseismicity and almost all rockbursts occur close to existing or newly-formed fracture zones (Ma et al 2018a). (2) weak fracture properties.…”

Section: A Conceptual Model For Mining-induced Microseismicitymentioning

confidence: 59%

“…Likewise, mine-induced microseismicity is in essence the slippage of pre-existing subsurface fractures triggered by mining activities. Recent publications confirmed that hypocentres of mining-induced microseismicity are bound to preexisting subsurface structures such as faults or fracture-rich regions (Hassani et al 2018;Ma et al 2018a). Therefore, the role of natural fractures throughout the active mining region needs to be highlighted in the explanation of microseismic response to coal extraction.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Mining Intensity and Pre-existing Fracture Attributes on Spatial, Temporal and Magnitude Characteristics of Microseismicity in Longwall Coal Mining

et al. 2020

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Knowledge regarding microseismic characteristics associated with longwall coal mining is crucial in evaluating the potential for underground mining hazards. Although microseismicity is induced by mining activities, it still remains uncertain as to what extent mining activities influence the spatial, temporal, and magnitude characteristics of microseismicity. To establish a thorough understanding of the relationship between microseismic characteristics and mining activities, a 27-month long microseismic monitoring campaign was conducted around a highly stressed coal zone and eight producing longwall panels at Coal Mine Velenje in Slovenia. Each microseismic event was classified to be associated with the producing longwall panel that triggered it, and the microseismic response to multi-panel longwall top coal caving face advance was analysed. Monitoring data have shown that locations of microseismic events coincided with stress concentrated regions. It was established that both seismic count and energy-intensive regions associated with coal mining in different panels are spatially connected, but they do not fully overlap with mined-out or stress concentrated areas. In addition, microseismic event counts frequency was found to be well correlated with mining intensity, while seismic energy magnitude and spatial distribution are poorly correlated with the same. Therefore, microseismic characteristics could not be explained solely by the mining-induced stress transfer and mining intensity, but are believed to be dominated by pre-existing natural fractures throughout the coal seam. Analyses of these observations helped the development of a conceptual seismic-generation model, which provides new insights into the causes of microseismicity in coal mining.

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Section: A Conceptual Model For Mining-induced Microseismicitymentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

The Role of Mining Intensity and Pre-existing Fracture Attributes on Spatial, Temporal and Magnitude Characteristics of Microseismicity in Longwall Coal Mining

et al. 2020

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“…Feng et al proposed a neural network prediction model based on the microseismic monitoring information [1]. Ma et al studied the seismic source, seismogenic period, and scale effect of rockburst and believed that rockburst can be predicted based on the relationship between rock heterogeneity and acoustic emission sequence [32]. Xu et al applied the ideal-point method to predict rockburst based on the information theory [33].…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Model for Rockburst Prediction Based on Microseismic Monitoring

Zhao

Chen

Zhu

2021

Advances in Civil Engineering

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Rockburst is an extremely complex dynamic instability phenomenon for rock underground excavation. It is difficult to predict and evaluate the rank level of rockburst in practice. Microseismic monitoring technology has been adopted to obtain microseismic events of microcrack in rock mass for rockburst. The possibility of rockburst can be reflected by microseismic monitoring data. In this study, a decision tree was used to extract the knowledge of rockburst from microseismic monitoring data. The predictive model of rockburst was built based on microseismic monitoring data using a decision tree algorithm. The predictive results were compared with the real rank of rockburst. The relationship between rockburst and microseismic feature data was investigated using the developed decision tree model. The results show that the decision tree can extract the rockburst feature from the microseismic monitoring data. The rockburst is predictable based on microseismic monitoring data. The decision tree provides a feasible and promising approach to predict and evaluate rockburst.

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“…us, a proper understanding of generating behaviors of a rockburst from low-energy tremors to the occurrence of strong tremors, and the rockburst-triggering criterion that determines the occurrence of rockbursts can provide useful insights into the evaluation of rockburst hazard as well as revealing their causes. However, diverse geological conditions, mining environments, and changing mining-induced stress make the evolution of microfractures in coal-rock masses extremely complex, bringing the di culty in precisely identifying the complicated evolution of tremors [4][5][6][7][8][9][10]. As the tremor evolution corresponds to the occurrence and development process of microcracks in coal-rock masses, the spatial tremor distribution is extremely complex and evolved in a nonlinear manner over time.…”

Section: Introductionmentioning

confidence: 99%

Generating Behaviors of Strong Tremors and Experimental Study of Rockburst‐Triggering Criterion

Wang

Jin

Gong

et al. 2019

Shock and Vibration

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Numerous tremors occur during excavation and mining periods in coal mines, and low-energy tremors often occur before strong tremors. In addition, it is found that not all strong tremors cause rockbursts. Therefore, generating behaviors of strong tremors and the triggering criterion for the occurrence of the rockburst must be investigated. In this study, generating behaviors of strong tremors were inferred by velocity tomograms and energy density clouds. An innovative facility capable of applying dynamic disturbance to roadway models was developed to study the triggering criterion for rockbursts. Velocity tomograms indicated that the stress concentration extended to the syncline region with the advance of the coalface. High-energy-density clouds expanded rapidly and regularly in some areas that were parts of the high-velocity region, indicating that the stress increased rapidly in these areas, until strong tremors took place nearby. AE activities suggested that the modelled roadway offered good resistance as the dynamic loading energy grew from 29.4 J to 117.6 J. Then, sharp AE activity at the dynamic loading energy of 147 J indicated the ultimate shock resistance of the roadway was almost reached. Finally, bursting failure of the modelled roadway was observed at a dynamic loading energy of 176.4 J.

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Rockburst mechanism and prediction based on microseismic monitoring

Cited by 147 publications

References 17 publications

The Role of Mining Intensity and Pre-existing Fracture Attributes on Spatial, Temporal and Magnitude Characteristics of Microseismicity in Longwall Coal Mining

The Role of Mining Intensity and Pre-existing Fracture Attributes on Spatial, Temporal and Magnitude Characteristics of Microseismicity in Longwall Coal Mining

Decision Tree Model for Rockburst Prediction Based on Microseismic Monitoring

Generating Behaviors of Strong Tremors and Experimental Study of Rockburst‐Triggering Criterion

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