Multiple indicators prediction method of rock burst based on microseismic monitoring technology

Wang, Shuren; Li, Chunyang; Yan, Wenfa; Zou, Zhengsheng; Chen, Wenxue

doi:10.1007/s12517-017-2946-8

Cited by 16 publications

(8 citation statements)

References 14 publications

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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]. A group of studies were performed to forecast the rockburst based on MS monitoring, and indeed obtained many useful findings [26], [27].…”

Section: Introductionmentioning

confidence: 99%

“…MS waves before the rockburst were investigated, suggesting that characteristics of the wave's velocity, amplitude, and frequency as well as rock stress state can be used for the rockburst warning [28]- [36]. The relationship between MS waves and the rockburst was analysed, indicating the features extracted from MS waves together with rock stress can be utilized to predict rockburst [25], [37]- [41].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Combining seismological theory with the analysis of various seismic data (such as seismic deformation, stress adjustment, energy release, etc.) of rock 2 of 17 mass deformation and failure process, the activity law of potential disasters could be determined, and early warning could be possible to achieve dynamic analysis, control rock mass stability, and protect staff and equipment [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel

Zhang

2019

Energies

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The high-stress hazards of underground engineering have stimulated the exploration of microseismic monitoring and early warning methods. To achieve a good monitoring effect, the monitoring object is usually enclosed by a microseismic array (sensor array) (e.g., slope engineering, etc.). However, some characteristics of a buried tunnel, including “linear”, “deep-buried”, and “long”, make it difficult to deploy a reasonable microseismic array, which leads to the microseismic array being non-enclosed for the monitoring object. Application of the non-enclosed microseismic array yields decreases the accuracy of the source location. To solve the problem wisely, this paper deals with the feasibility of non-enclosed microseismic arrays (axial-extended, lateral-extended, and twin-tube arrays) by introducing a quantitative method. To this end, an optimized microseismic array with the best source location accuracy for a twin-tube expressway tunnel is proposed. The obtained results reveal that the non-enclosed microseismic arrays, which are unavoidable in expressway tunnel engineering, do not introduce errors but reduce the ability to resist them. Further, the twin-tube array achieves a better source location accuracy than the axial and lateral-extended arrays. In the application of the source location based on the particle swarm optimization (PSO) algorithm to the twin-tube array, microseismic events, which cluster in the rockburst section, are wholly gathered, and the maximum error is reduced by about 30–50 m, indicating its greater feasibility with respect to the single-tube array.

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“…Mansurov demonstrated that the microseismic signal is a highly efficient and accurate predictor of strong impact ground pressure [9]. Wang et al conducted the 80-day monitoring at the Qixing Coal Mine through a high-precision microseismic monitoring system [10]. Zhang et al studied the correlation length of mine microseismic signals at different spatial scales with a single-link cluster method and analyzed its mechanism [11].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

Wang

Lyu

et al. 2019

Shock and Vibration

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An experiment designed to simulate coal during excavation was conducted. Microseismic signals of coal under vibration conditions during excavation and subsequent waiting time of the coal roadway at different excavation speeds were collected and analyzed. During the excavation and subsequent waiting time, the stress in coal is redistributed, and the concentrated stress is gradually transferred to the deeper section of the coal seam. The Hilbert–Huang transform (HHT) is used to effectively denoise the collected signals. According to the noise-reduced signal, the amplitude and pulse number of the microseismic signals emitted during the excavation process are much larger than those of the waiting time process. During excavation, the energy and event numbers of microseismic signals increase first and then decrease as the excavation speed increases. The faster the excavation speed, the more the energy, and the higher the event numbers of the microseismic signals released during the subsequent waiting time. When the excavation speed is faster, more elastic potential accumulates in the coal seam and the concentration stress is greater. As the concentrated stress moves forward in time without excavation, more coal seams fail, and more microseismic signals are released. The microseismic signal and the stress evolution law can provide a reasonable explanation for the forward movement of the concentrated stress and coal failure during roadway excavation.

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Multiple indicators prediction method of rock burst based on microseismic monitoring technology

Cited by 16 publications

References 14 publications

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

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

Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel

Experimental Study on Stress Evolution and Microseismic Signals under Vibration Conditions of Coal during Excavation and Subsequent Waiting Time

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