Developments and prospects of microseismic monitoring technology in underground metal mines in China

Liu, Jianpo; Si, Ying-tao; Wei, Deng-cheng; Shi, Hong-xu; Ren, Wang

doi:10.1007/s11771-021-4839-y

Cited by 24 publications

(5 citation statements)

References 80 publications

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“…However, the stability of the goaf determines the prospects for the utilization of underground space resources in closed mines. If the stability of the goaf is poor, it not only restricts the utilization of space resources but also poses risks of collapse or geological changes, which may lead to damage to surrounding structures and affect their safe operation (Liu et al 2021;Li et al 2023; Zhang et al 2023b). Therefore, studying the stability of the goaf in the Sanhejian closed coal mine is crucial for its redevelopment and utilization.…”

Section: Introductionmentioning

confidence: 99%

Stability evaluation of goaf in closed mining area: a case study of Sanhejian closed mining area in Jiangsu Province, China

Shi,

Zhang,

Zhang

et al. 2024

Preprint

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The stability of goaf is one of the decisive conditions for the redevelopment and utilization of underground spaces after mine closure. Taking the Sanhejian closed mine area as an example, this study comprehensively evaluates the stability of the goaf using numerical simulation, Analytic Hierarchy Process (AHP), and Vulnerability Index (VI). Firstly, the numerical model of the goaf was built using FLAC3D software to obtain the stress field, displacement field, and characteristics of plastic zone development. Based on the simulation results, stability evaluation criteria for the goaf were formulated, and stability levels were determined. Secondly, a vulnerability assessment model was established using AHP, selecting geological factors, mining factors, and hydrological factors as primary indicators and further determining eight secondary indicators, including geological structural complexity, roof lithology and thickness, geostress, stop mining time, depth-to-coal ratio, goaf width, goaf area, and water volume in goaf. The weights of each indicator were determined, and the indicators were quantified to calculate the VI value of the vulnerability assessment model. The stability zoning threshold of the goaf was obtained using a natural breakpoint classification method and verified against the numerical simulation results to enhance the accuracy of stability evaluation. By integrating the results of both methods and adhering to a conservative risk assessment principle, the stability level of the goaf was ultimately determined, providing reference for the stability evaluation of related underground spaces.

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Section: Introductionmentioning

confidence: 99%

Stability evaluation of goaf in closed mining area: a case study of Sanhejian closed mining area in Jiangsu Province, China

Shi,

Zhang,

Zhang

et al. 2024

Preprint

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“…Chen et al (2015) used microseismic monitoring technology to deeply study the rock mass damage and rockburst in the deep-buried tunnel of Jinping II Hydropower Station. Based on the experimental study of rock failure acoustic emission, Liu et al (2021) established a prediction method of rock burst disasters of mine. Microseismic monitoring technology has been widely used in rock engineering under high stress, and has become a common means of deep engineering disaster research (Li et al, 2022;Li et al, 2023a.…”

Section: Introductionmentioning

confidence: 99%

Mechanical performance and failure mechanism of U-steel support structure under blast loading

Zhao,

Yang,

et al. 2024

Front. Earth Sci.

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The U-steel support structures of underground caverns are prone to instability and failure under blast loads. The purpose of the underground cavern reinforcement is to mobilise the self-supporting capacity of the surrounding rock to resist the blast. To better understand the mechanical performance and failure mechanism of the U-steel support, the fracture process and vibration behaviour of the support structure under blast loading are investigated by the microseismic monitoring experiment. The dynamic responses of the cavern support structures under blast loading are investigated, and the potentially hazardous sections of the U-steel support structure are revealed by the theoretical analysis. The microseismic monitoring results show that the blast induced microseismic events are concentrated in the arch shoulder of the small chainage, correspondingly the U-steel structures in this region have been partially extruded and deformed. The failure mechanism of the supporting structure is presented. In order to effectively inhibit the internal fracture evolution or macroscopic failure of the rock mass, the synergetic reinforcement scheme of the structures is proposed. The results of the research can be used as a reference for the design and control method of the U-steel support in similar projects.

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“…Artificial intelligence methods have great advantages in trend prediction, pattern recognition or classification of data and dealing with highly nonlinear uncertainties in the data [15][16]. Artificial energy intelligence methods overcome the shortcomings of traditional methods that require accurate models and are able to solve problems that are difficult or even impossible to solve in traditional computational methods [17]. For the tailings pond safety trend prediction problem, the artificial intelligence method can avoid the precise modeling process of tailings pond data and simply use the existing data to train the parameters so that the model can fully reflect the highly nonlinear, data-intrinsic relationships within the data that are difficult to portray [18][19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of quantitative management of online intelligent monitoring of tailing ponds based on the perspective of safety prevention and control

Zhang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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China’s tailing pond online monitoring technology started late, and the tailing pond is located in a harsh working environment, for the limitations of traditional manual monitoring of tailing pond, combined with the actual situation of Zhenhua Mining tailing pond. This paper constructs a risk monitoring index system and online monitoring early warning model based on (Language Model - Back Propagation, LM-BP) neural network to quantitatively assess tailing pond safety risks and analyze and judge safety risk trends. We extracted common indicators of regional tailing ponds, combined with meteorological data to establish a regional safety risk assessment model, integrated vulnerability of disaster-bearing bodies, environmental sensitivity and other influencing factors, realized regional risk coupling analysis, and dynamically built a risk cloud map. Based on the perspective of safety risk prevention and control, the integrity and accuracy of monitoring data are analyzed, the causes of early warning are inverted, alarm disposal mechanisms are established, and closed-loop management of early warning is realized to provide scientific auxiliary decision-making support for tailing pond safety supervisors.

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Developments and prospects of microseismic monitoring technology in underground metal mines in China

Cited by 24 publications

References 80 publications

Stability evaluation of goaf in closed mining area: a case study of Sanhejian closed mining area in Jiangsu Province, China

Stability evaluation of goaf in closed mining area: a case study of Sanhejian closed mining area in Jiangsu Province, China

Mechanical performance and failure mechanism of U-steel support structure under blast loading

Analysis of quantitative management of online intelligent monitoring of tailing ponds based on the perspective of safety prevention and control

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