China University of Mining and Technology (CUMT)

Xuzhou Hongyi Technology Development Co., Ltd.

Xuzhou Wushou Information Technology Co., Ltd.

Xu Yang

Anye Cao

Yaoqi Liu

Changbin Wang

Geng Li

Chengchun Xue

Xianxi Bai

Yujie Peng

Zhifeng Ma

Qiang Niu

Linming Dou

Siyuan Gong

Wu Cai

Qing Ge

Jianqiu Xue

Guowei Lyu

Yingyuan Wen

Guowei Lv

Fan Chen

Wenhao Guo

Songwei Wang

A rock burst hazard prediction method based on vibration wave energy attenuation characteristics of a mine earthquake cluster is provided. The rock burst hazard prediction method comprehensively considers the static load and dynamic load effects of the vibration waves of the mine earthquake cluster based on vibration wave energy attenuation characteristics of the mine earthquake cluster. The static load strength index and the dynamic load strength index involved in the method have clear physical meanings. A comprehensive prediction index calculation model proposed based on the dynamic and static load superposition principle of rock burst occurrence is clear, and the method has a firm theoretical support as well as strong universality and operability. Meanwhile, the updating and adjustment of weights are rapid and the objective judgment and prediction of the final comprehensive prediction results are efficient, and the high-energy mine earthquake and impact behavior area can be effectively predicted.

Rock burst hazard prediction method based on seismic wave energy attenuation characteristics of mine earthquake cluster

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

Method for assessing stability of roadway surrounding rock based on numerical simulation and deep learning

An online stress monitoring system is used to obtain a stress distribution of each position, a geophone monitoring system is used to monitor high-frequency vibration signals produced by coal and rock fracture, a support resistance monitoring system is used to monitor working resistance of hydraulic supports in a working face, a well-ground joint microseismic system is used to monitor low-frequency vibration signals produced by the coal and rock fracture, and realize position of a microseismic source and inversion imaging of mining fractures, and an anchor stress monitoring system is used to monitor stress states of anchor bolts and cables. A data processing center is used to comprehensively process data obtained from the above five systems using an early warning model, thereby to obtain a prediction result of rock burst and determine whether to make early warning, thus workers can take timely measures according to the early warning situation.

Intelligent monitoring and early warning device and method for rock burst based on multi-field and multi-source information fusion

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Xuzhou, China

Xu Yang