Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

Zhou, Jian; Shen, Xiaojie; Qiu, Yingui; Shi, Xiuzhi; Khandelwal, Manoj

doi:10.1016/j.tust.2022.104570

Cited by 42 publications

(12 citation statements)

References 44 publications

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“…Early detection of these gases is crucial in preventing accidents and ensuring a safe working environment. GPS (Global Positioning System) tracking systems (A10) [ 31 , 32 , 33 ] in underground mines contribute significantly to securing health and safety by providing accurate location information for personnel, equipment, and vehicles within the mining environment. Despite the challenges of limited visibility and confined spaces in underground mines, GPS tracking systems enhance situational awareness, streamline operations, and play a crucial role in emergency response planning.…”

Section: Methodsmentioning

confidence: 99%

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Wang,

Cheng,

et al. 2024

Sensors

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This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

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Section: Methodsmentioning

confidence: 99%

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Wang,

Cheng,

et al. 2024

Sensors

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“…Generally, R 2 and VAF values equal to 100, and RMSE, MAE, and MAPE values equal to 0 indicate the best predictive performance of a model. The interpretation of these indicators is given in the literature ( 75 – 89 ), and their expressions are shown in Equations (22–26).

…”

Section: Methodsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253).

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“…The source location is of great importance for risk assessment of impend ing mining-induced instabilities. Several location methods have been developed to im prove the microseismic location accuracy, e.g., the nonlinear method, methods withou the pre-measured wave velocity or arrival time picking, the collaborative method based on analytical and iterative solutions, and the optimized relative location method [50][51][52][53][54] The sensor layout is a key contributing factor influencing microseismic location ac curacy. Longwall extraction is the most popular mining method in China, and microseis mic sensors in coal mines are usually distributed in a plane around the longwall panel…”

Section: Microseismic Source Location and Sensor Layoutmentioning

confidence: 99%

Section: Microseismic Source Location and Sensor Layoutmentioning

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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Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

Cited by 42 publications

References 44 publications

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

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

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