Multi-Index Geophysical Monitoring and Early Warning for Rockburst in Coalmine: A Case Study

Liu, Xiaofei; Zhang, Siqing; Wang, Enyuan; Zhang, Zhibo; Yong, Wang; Yang, Shengli

doi:10.3390/ijerph20010392

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…In Equation (6), Ri 0 represents the distance from event S0 to station i, which can be expressed by Equation (7), and Vi is the velocity model determined by the active source SS, which can be expressed by Equation (8):…”

Section: Location Methods Using the "Source-station" Velocity Modelmentioning

confidence: 99%

“…Unlike other monitoring technologies, microseismic monitoring technology is capable of describing the spatiotemporal distribution of microseismic events in the monitored area. Therefore, the precise location of microseismic events is of utmost importance for most of the rockburst prediction methods based on microseismic monitoring, which are currently reliant on the statistical features of these events [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

Shen

Wang

Jiang

et al. 2023

Sensors

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The velocity model is one of the main factors affecting the accuracy of microseismic event localization. This paper addresses the issue of the low accuracy of microseismic event localization in tunnels and, combined with active-source technology, proposes a “source–station” velocity model. The velocity model assumes that the velocity from the source to each station is different, and it can greatly improve the accuracy of the time-difference-of-arrival algorithm. At the same time, for the case of multiple active sources, the MLKNN algorithm was selected as the velocity model selection method through comparative testing. The results of numerical simulation and laboratory tests in the tunnel showed that the average location accuracy of the “source–station” velocity model was improved compared with that of the isotropic velocity and sectional velocity models, with numerical simulation experiments improving accuracy by 79.82% and 57.05% (from 13.28 m and 6.24 m to 2.68 m), and laboratory tests in the tunnel improving accuracy by 89.26% and 76.33% (from 6.61 m and 3.00 m to 0.71 m). The results of the experiments showed that the method proposed in this paper can effectively improve the location accuracy of microseismic events in tunnels.

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Section: Location Methods Using the "Source-station" Velocity Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Anisotropic Velocity Model for Microseismic Events Localization in Tunnels

Shen

Wang

Jiang

et al. 2023

Sensors

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“…Otherwise, the color video image is continuously monitored to identify whether the black area changes; (6) The methane sensors monitor whether the methane concentration in the monitoring area increases rapidly or reaches the alarm value. When multiple different locations, such as the mining face, air inlet tunnel, air return tunnel, and total air return tunnel, detect a significant increase or reach the alarm value, the alarm will be issued for a coal and gas outburst, and vice versa, where the alarm will be issued for a rock burst; (7) If the emergency response is activated after the alarm of coal and gas outbursts, then the alarm is canceled and there is continued monitoring and identification as to whether there is a black area change in the color video image. Otherwise, the disaster alarm is continued and the emergency response is activated; if the emergency response is activated after the alarm of rock burst, then the alarm is canceled and there is continued monitoring and identification as to whether there is a black area change in the color video image.…”

Section: Video Image Perception Methodsmentioning

confidence: 99%

“…The color camera installation position from the installation height of the roadway floor to the lowest height of the roadway section was h = 1.8 m, calculated according to Equation (7), with a color camera in such conditions having the best angle of inclination of about 7 • . Further, according to Equation (10), the best angle of inclination of a color camera can identify the maximum speed of about 162 m/s, much greater than the set speed threshold v t = 13 m/s, so in an underground coal mine without a high-speed color camera, the use of an ordinary color camera can identify the speed characteristics of the thrown coal rocks when the disaster occurs.…”

Section: Selection Of Camera Typementioning

confidence: 99%

“…At present, the main methods of disaster monitoring are rock mechanics methods, which are mainly based on drilling cuttings and stress monitoring. The geophysical methods mainly include the microseismic method, acoustic emission method, and electromagnetic radiation method [6][7][8][9][10][11][12]. There have also been disaster hazard studies based on physical experiments using images [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Coal Mine Rock Burst and Coal and Gas Outburst Perception Alarm Method Based on Visible Light Imagery

Cheng,

Liu,

2023

Sustainability

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To solve the current reliance of coal mine rock burst and coal and gas outburst detection on mainly manual methods and the problem wherein it is still difficult to ensure disaster warning required to meet the needs of coal mine safety production, a coal mine rock burst and coal and gas outburst perception alarm method based on visible light imagery is proposed. Real-time video images were collected by color cameras in key areas of underground coal mines; the occurrence of disasters was determined by noting when the black area of a video image increases greatly, when the average brightness is less than the set brightness threshold, and when the moving speed of an object resulting in a large increase in the black area is greater than the set speed threshold (V > 13 m/s); methane concentration characteristics were used to distinguish rock burst and coal and gas outburst accidents, and an alarm was created. A set of disaster-characteristic simulation devices was designed. A Φ315 mm white PVC pipe was used to simulate the roadway and background equipment; Φ10 mm rubber balls were used to replace crushed coal rocks; a color camera with a 2.8 mm focal length, 30 FPS, and 110° field angle was used for image acquisition. The results of our study show that the recognition effect is good, which verifies the feasibility and effectiveness of the method.

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