Results 41 to 50 of about 3,230 (205)

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters

Sensors, 2021
Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering.
Kang Peng, Zheng Tang, Longjun Dong, Daoyuan Sun   +3 more
doaj   +1 more source

Application of CT inversion monitoring and early warning technology in microseismic anomaly area

Gong-kuang zidonghua, 2021
In order to solve the problems that microseismic monitoring can only reflect the danger of the area where the microseisms are located, and CT inversion monitoring cannot reflect the current danger level of the working face in time, taking the 3105 ...
LI Yunsheng1, XU Desheng1, MA Zhifeng2, ZHOU Haijun1, GUO Wenhao3   +4 more
doaj   +1 more source

An optimizing microseismic method for rock burst early warning based on mining production process

Deep Underground Science and Engineering, EarlyView.
A classification early warning method of rock burst based on hourly microseismic data is proposed, which can be combined with the on‐site production process to provide more timely warning. Abstract Microseismic (MS) events have been reported in nearly every coal mining country, which could well lead to rock burst in underground coal mines.
Zepeng Han, Linming Dou, Wu Cai, Mingbo Chi, Guojun Xi, Shuai Chen, Biao Jiao   +6 more
wiley   +1 more source

Microseismic monitoring of the controls on coastal rock cliff erosion [PDF]

, 2012
The aim of this thesis has been to improve understanding of the controls on coastal rock cliff erosion, utilising microseismic ground motion. Coastal cliff erosion has remained poorly understood, in part confounded by the challenges associated with ...
NORMAN, EMMA,CATHERINE, Norman, Emma Catherine   +1 more
core  

Fracture evolution of a thick soft protection layer and the water inrush mechanism in overburden under longwall mining

Deep Underground Science and Engineering, EarlyView.
Through shear–tensile creep tests and viscoelastic modeling, the fracture evolution of thick soft protective layers is clarified. Results show thickness‐dependent rheological failure modes that govern four types of roof water inrush, providing a mechanism‐based framework for hazard prediction and control. Abstract In the Jurassic coal‐bearing strata of
Mengnan Liu, Wei Qiao, Xianggang Cheng, Xiangsheng Meng, Peichao Feng   +4 more
wiley   +1 more source

Genetic Programming-Based Prediction Model for Microseismic Data

Geofluids, 2022
Microseismic monitoring is a rock breakdown monitoring technology, and it has become a major technical tool for underground disaster warning and prevention.
Man Wang, Hongwei Zhou, Dongming Zhang, Yingwei Wang, Weihang Du, Beichen Yu   +5 more
doaj   +1 more source

Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture

Applied Sciences, 2023
In microseismic monitoring, the reconstruction of a reliable velocity model is essential for precise seismic source localization and subsurface imaging.
Yixiu Zhou, Liguo Han, Pan Zhang, Jingwen Zeng, Xujia Shang, Wensha Huang   +5 more
doaj   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

A method for predicting the time series of microseismic events in coal mines based on modal decomposition and deep learning

Meitan xuebao
Microseismic monitoring data plays a crucial role in predicting and warning dynamic hazards such as coal mine rockburst. Utilizing historical microseismic monitoring data to forecast the evolution characteristics of future microseismic events is an ...
Changkun QIN, Wusheng ZHAO, Haibin JIA, Weiji GAO, Yuchuan CHEN, Wanquan YU, Jizhong LIANG, Weizhong CHEN   +7 more
doaj   +1 more source

Advances in vital‐sign prediction and early‐warning models for underground coal mine workers integrating environmental factors

Deep Underground Science and Engineering, EarlyView.
This review synthesizes advances in predicting miners' vital signs by integrating environmental monitoring (dust, temperature, and gas) with physiological data. It highlights multi‐source data fusion techniques and early‐warning models for enhanced occupational safety in underground coal mines.
Junji Zhu, Dawei Cui, Yingjie Liu, Yuntao Liang, Yue Zhang   +4 more
wiley   +1 more source