9. Microseismic activity associated with hydraulic mining

Sato, Kazuhiko; Isobe, Tetsuhiko; Môri, N.; Goto, Takashi

doi:10.1016/0148-9062(86)91669-4

Cited by 11 publications

(2 citation statements)

References 6 publications

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“…The physical process of rockburst can be described by damage mechanics (Sato et al, 1986). Due to the existence of natural damage in rock, local tensile damage occurs due to excavation induced compressive stress (Leighton, 1982).…”

Section: Rockburst Prediction and Criteriamentioning

confidence: 99%

Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station

Tang

et al. 2015

Tunnelling and Underground Space Technology

184

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Section: Rockburst Prediction and Criteriamentioning

confidence: 99%

Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station

Tang

et al. 2015

Tunnelling and Underground Space Technology

184

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“…MS monitoring analyses the precursor characteristics of the rockburst by observing micro-fractures, resulted from local damage inside the rock mass under the external force disturbance [23], [24]. The MS monitoring technique has several advantages, including without effect on production activities, and monitoring MS events of the whole mining area in real-time [25].…”

Section: Introductionmentioning

confidence: 99%

Investigate Contribution of Multi-Microseismic Data to Rockburst Risk Prediction Using Support Vector Machine With Genetic Algorithm

Xie

Wang

et al. 2020

IEEE Access

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As a severe hazard in coal mining and rock excavation, the rockburst is usually induced by the high energy tremor. Microseismic (MS) monitoring is suggested to forecast the rockburst risk to reduce its damage. The paper aims to investigate contribution of multi-MS data, including MS raw wave data and MS energy data, to prediction of the high energy tremor, using support vector machine (SVM) together with genetic algorithm (GA). MS monitoring data recorded for more than 400 days at Wudong coal mine of Xinjiang, China, were used in the paper. 132 and 24 features are initially extracted from MS raw wave and energy data in the frequency domain, entropy and time-frequency domain, respectively. GA is not only used to select effective ones among initially extracted features, but also optimize hyperparameters for SVM to classify high energy tremors from general MS events. The performances of the proposed approach based on multi-MS data are evaluated by cross-validation. The results show that the classifier achieves 98% sensitivity, 88% accuracy and 87% specificity using both MS raw wave and energy data, which is better than solely utilizing MS raw wave (98% sensitivity, 84% accuracy and 83% specificity) or energy data (98% sensitivity, 86% accuracy and 85% specificity). These findings suggest that MS raw wave data makes important contribution to rockburst risk prediction as well as MS energy data, and the better performance can be achieved when utilizing two kinds of data simultaneously. INDEX TERMS Rockburst risk prediction, microseismic monitoring, microseismic raw wave data, support vector machine, genetic algorithm.

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Mechanism and Predicting Theory-Based of Rockburst Evolution

Wang

2017

Evolution, Monitoring and Predicting Models of Rockburst

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The dynamic hazard, caused by failure of surrounding rockmass because of rock excavation, is the most direct danger of mining safety and efficient production. Many scholars (Wang et al. 2014(Wang et al. , 2015 Lindin and Lobanova 2013;Chen et al. 2015;He et al. 2010;Pan et al. 2014) paid much attention to it. Although many achievements were be made in theoretical and field cases, there is no unified understanding of rock dynamic hazards, especially the mechanism and predicting of rockburst, and further research is needed. Previous researches on rock failure and rockburst induced conditions have been carried out, but the mechanism and predicting of rockburst is complicated, and there is no mechanism and prediction model to explain the occurrence of rockburst satisfactorily. Therefore, the study of predicting rockburst induced conditions and the failure behavior of rock fracture is analyzed by proper theory. Most of these can provide reasonable suggestions for mining design; mining method selection and rockburst hazard prevention measures. It is of great significance for mine safety and high efficiency mining. In complex mining conditions, in order to solve the key technical problems of predicting rockburst, first of all, it is necessary to analyze the conditions induced rockburst, so as to lay the foundation for predicting of rockburst. The essence of rock failure is the release of elastic strain energy stored in rock under the action of stress. Therefore, one of key factors to study rock failure is the stress distribution in rock and the relation between rock elastic energy and dissipation energy per unit volume. If most of energy absorbed by rock is dissipated during loading, then the final failure mode of rock will be static. On the contrary, it is dynamic.On the basis of these, combined with the deformation characteristics of rocks, the author explored the stress-energy mechanism of rockburst. Since the 1970s, the emergence of nonlinear theory greatly promoted the development of rock mechanics such as mutation theory, dissipative structure theory, fractal structure theory etc. Using these theories to study rock failure and rockburst, it not only

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9. Microseismic activity associated with hydraulic mining

Cited by 11 publications

References 6 publications

Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station

Rockburst characteristics and microseismic monitoring of deep-buried tunnels for Jinping II Hydropower Station

Investigate Contribution of Multi-Microseismic Data to Rockburst Risk Prediction Using Support Vector Machine With Genetic Algorithm

Mechanism and Predicting Theory-Based of Rockburst Evolution

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