Evolution Characteristics of Seismic Detection Probability in Underground Mines and Its Application for Assessing Seismic Risks—A Case Study

Li, Hui; Cao, Anye; Gong, Siyuan; Wang, Changbin; Zhang, Rupei

doi:10.3390/s22103682

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Wang and colleagues proposed a workflow for reprocessing seismic data aimed at improving the quality of seismic data to better analyze changes in seismic activity [29,30]. Li and others investigated the evolution characteristics of the detection probability of seismic monitoring systems during the retreat mining period of coal mining faces and further correlated highly integrated seismic data with seismic risk [31].…”

Section: Introductionmentioning

confidence: 99%

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

Wang,

Zhan,

Zheng

et al. 2024

Mathematics

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Microseismic data are widely employed for assessing rockburst risks; however, significant disparities exist in the monitoring capabilities of seismic networks across different mines, and none can capture a complete dataset of microseismic events. Such differences introduce unfairness when applying the same methodologies to evaluate rockburst risks in various mines. This paper proposes a method for assessing the monitoring capability of seismic networks applicable to heterogeneous media in mines. It achieves this by integrating three gradient boosting algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). Initially, the isolation forest algorithm is utilized for preliminary data cleansing, and feature engineering is constructed based on the relative locations of event occurrences to monitoring stations and the working face. Subsequently, the optimal hyperparameters for three models are searched for using 8508 microseismic events from the a Coal Mine in eastern China as samples, and 18 sub-models are trained. Model weights are then determined based on the performance metrics of different algorithms, and an ensemble model is created to predict the monitoring capability of the network. The model demonstrated excellent performance on the training and test sets, achieving log loss, accuracy, and recall scores of 7.13, 0.81, and 0.76 and 6.99, 0.80, and 0.77, respectively. Finally, the method proposed in this study was compared with traditional approaches. The results indicated that, under the same conditions, the proposed method calculated the monitoring capability of the key areas to be 11% lower than that of the traditional methods. The reasons for the differences between these methods were identified and partially explained.

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Section: Introductionmentioning

confidence: 99%

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

Wang,

Zhan,

Zheng

et al. 2024

Mathematics

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“…These are very real issues that mining companies have to consider when it comes to providing safe working conditions for their employees. One of these is a seismic hazard that occurs in underground mines [3]. This type of natural hazard is incredibly difficult to detect and predictable, making it one of the most common threats in the mining industry.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Seismic Hazards in Underground Mine Operations using Machine Learning

Shende¹,

Ashok²,

Limkar³

et al. 2023

IJRITCC

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The most common causes of coal mining accidents are seismic hazard, fires, explosions, and landslips. These accidents are usually caused by various factors such as mechanical and technical failures, as well as social and economic factors. An analysis of these accidents can help identify the exact causes of these accidents and prevent them from happening in the future. There are also various seismic events that can occur in underground mines. These include rock bumps and tremors. These have been reported in different countries such as Australia, China, France, Germany, India, Russia, and Poland. Through the use of advanced seismological and seismic monitoring systems, we can now better understand the rock mass processes that can cause a seismic hazard. Unfortunately, despite the advancements, the accuracy of these methods is still not perfect. One of the main factors that prevent the development of effective seismic hazard prediction techniques is the complexity of the seismic processes. In order to carry out effective seismic risk assessment in mines, it is important that the discrimination of seismicity in different regions is carried out. The widespread use of machine learning in analyzing seismic data, it provides reliability and feasibility for preventing major mishaps. This paper provides uses various machine learning classifiers to predict seismic hazards.

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“…Based on dynamic static energy principles, He et al [28] studied the precursory rules of coal bumps combined with electromagnetic emission and microseismic monitoring, and established a coupling evaluation system according to the monitoring results of a coal mine. Li et al [29] investigated the effect of the number of seismic data used for calculations and modifications to the arrangement of seismic monitoring transducers on the method of detecting earthquake probability. It can be concluded that these research results are mainly about risk assessment considering different geological factors.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Assess Thick, Hard Roof-Induced Rock Burst Risk Based on Mining Speed Effect on Key Energy Strata

et al. 2022

Sustainability

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Roof-type rock burst (RTRB) frequently occurs in the hard, thick roof of working faces, which causes roadway failure, facility damage and even personnel casualties. Previous research results show that mining speed has obvious effects on the rock burst risk and many rock burst accidents are caused by an unreasonable mining speed. To provide a theoretical foundation for the determination of a reasonable mining speed in a specific working face subjected to RTRB, in this study, the key energy strata (KES) principle contraposing the RTRB was proposed, and the criterion of KES was determined by defining the energy release coefficient kc. On this basis, the energy accumulation characteristics of coal and energy release of surrounding rock were analyzed using FLAC3D numerical simulation. Accordingly, to assess the rock burst risk considering the mining speed effect, a new method was proposed and a new energy index Φvi was defined to divide rock burst risk with different mining speeds into four grades. To validate the availability of the KES principle and the new assessment method, they were adopted in a thick, hard roof working face. The application results indicate that the mining speed of 3.6 m/d obtained by the method meets the demands of safe and high-efficiency production.

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Evolution Characteristics of Seismic Detection Probability in Underground Mines and Its Application for Assessing Seismic Risks—A Case Study

Cited by 4 publications

References 23 publications

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

A Method for Evaluating the Data Integrity of Microseismic Monitoring Systems in Mines Based on a Gradient Boosting Algorithm

Assessment of Seismic Hazards in Underground Mine Operations using Machine Learning

A New Method to Assess Thick, Hard Roof-Induced Rock Burst Risk Based on Mining Speed Effect on Key Energy Strata

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