Investigation of coal and gas outburst risk by microseismic monitoring

Wang, Anhu; Song, Dazhao; He, Xiaocong; Li, Dou; Zu, Ziyin; Lou, Quan; Zhao, Yingjie

doi:10.1371/journal.pone.0216464

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…The generation and mechanism of coal and gas outbursts during deep coal mining are complicated, and a catastrophic disaster suddenly ejects a significant quantity of coal and gas to the excavation face, which destroys underground structures and threatens worker safety [111]. Approximately 510 coal and gas outburst accidents occurred in China between 2001 and 2017, which resulted in 3576 deaths [112].…”

Section: Coal and Gas Outburstmentioning

confidence: 99%

Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review

Liu,

Wang,

Kou

et al. 2024

Applied Sciences

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China’s coal mines have to extend to greater depths for the exploitation of more mineral resources, and they have suffered catastrophic mining-induced disasters, such as rockbursts, water inrushes, coal and gas outbursts, and roof fall accidents. The microseismic monitoring technique is a practical tool for mine safety management, which is extensively utilized in many Chinese coal mines. Microcracks of coal/rock masses are recorded as microseismicities in the field, and the potential mining-induced instabilities can be assessed by in-depth analysis of the microseismic parameters. This study provides a state-of-the-art review of the achievements and developments of the microseismic monitoring technique in coal mines. It also presents some prospects for improving the location accuracy of microseismicity, efficient and intelligent processing of the microseismic data, comprehensive assessment of coal/rock instabilities, and development of new microseismic monitoring equipment. This study is valuable for mine safety management and may contribute to improving the deep mining production.

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Section: Coal and Gas Outburstmentioning

confidence: 99%

Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review

Liu,

Wang,

Kou

et al. 2024

Applied Sciences

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“…At present, the main forecasting methods include the rock mechanics method represented by the drilling cuttings method, geophysical methods represented by microseismic and electromagnetic radiation and acoustic emission, the empirical analogy method represented by the comprehensive index method, and the dynamic method of surrounding rock represented by the dynamic method of roof [9][10][11]. The microseismic monitoring system is a regional monitoring method, which can automatically record the microseismic activity and provide a basis for rockburst risk evaluation by real-time calculation of microseismic position and energy [12,13]. The principle is to utilize the different times to reach the starting point of the P wave received by the underground seismic substation 2 of 17 to perform two-dimensional and three-dimensional positioning.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of Fuzzy Comprehensive Evaluation Model for Rockburst Based on Microseismic Monitoring

Li,

Chen,

et al. 2023

Applied Sciences

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Based on the relationship between rockburst and microseismic event indicators, this study proposes that the risk of rockburst in mine working faces, roadways, and even the entire mine should be studied through the “double high” risk evaluation of microseismic events. The 43 evaluation indexes of rockburst were optimized into eight indexes by using the expert scoring method. Considering the eight indexes as the basic events and the “double high” risk of microseismic events as the top event, the “double high” accident tree of microseismic events was established. According to the qualitative analysis results of the accident tree, the microseismic activity evaluation index was determined, and the “double high” risk evaluation index system was constructed for microseismic events. The system included three first-level indicators and eight second-level indicators. The fuzzy hierarchical comprehensive evaluation model was used to evaluate the “double high” risk of microseismic events in the Yanbei Coal Mine. In this paper, a microseismic monitoring and evaluation index model is constructed to simplify the existing evaluation system, which is convenient to effectively establish the connection between microseismic monitoring data and rockburst index and provide important theoretical support for underground monitoring and rockburst prevention.

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“…In recent years, more and more scholars have studied the characteristics of coal and gas outburst, trying to find a method for accurately predicting outburst risk. Some experts and scholars apply engineering technologies and geophysical methods to predict the outburst risk, including the drill cuttings index method (the amount of drill cuttings S, the drill cuttings desorption index K 1 or Δh 2 ), the initial velocity of gas emission from drilling holes, the composite index method [16,17], acoustic emission (AE) technology [12,18,19], microseismic (MS) technology [20,21], and electromagnetic radiation (EMR) technology [22,23]. Some scholars input factors affecting coal and gas outburst into algorithms and models of machine learning such as Fisher Discriminant Analysis (FDA) [24] and neural network [25] to analyze the correlation between various factors and the risk of coal and gas outburst and realize the prediction of outburst risk [26,27].…”

Section: Introductionmentioning

confidence: 99%

Risk Prediction of Coal and Gas Outburst Based on Abnormal Gas Concentration in Blasting Driving Face

2022

Self Cite

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In order to realize dynamic, continuous, and real-time prediction of coal and gas outburst risk in real time in blasting driving face, an outburst risk prediction method based on the characteristics of gas emission after blasting is proposed. In this study, the causes of abnormal gas concentration in blasting driving face are analyzed, and the identification method of abnormal gas concentration based on weighted K-nearest neighbor (weighted KNN) is proposed. The correlation between gas emission characteristics after blasting and K1 value is analyzed, and the prediction model of outburst risk based on convolutional neural networks (CNN) is established and applied in Jinjia coal mine in China. The results show that the causes of abnormal gas concentration mainly include ventilation stop, blasting operation, sensor adjustment, and other abnormalities. The accuracy of the identification method is 86.1%. Especially, the identification accuracy of blasting operation is 92%. There are strong correlations between the growth rate, peak value, and decay rate of gas concentration after blasting and K1 value, and the maximum correlation coefficient is 0.92. Using the prediction model, 28 times of jet holes and 1 small outburst event are predicted successfully, and the efficiency of the prediction model is 76.39%. By this technology, the utilization rate of gas information is improved, and the relationship between the change characteristics of gas concentration after blasting and the risk of coal seam outburst is established, which is of significant for improving the prediction accuracy and risk management ability of coal and gas outburst.

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Investigation of coal and gas outburst risk by microseismic monitoring

Cited by 13 publications

References 24 publications

Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review

Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review

Construction and Application of Fuzzy Comprehensive Evaluation Model for Rockburst Based on Microseismic Monitoring

Risk Prediction of Coal and Gas Outburst Based on Abnormal Gas Concentration in Blasting Driving Face

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