Results 61 to 70 of about 5,383,519 (211)

Automatic picking method of microseismic first arrival time based on improved support vector machine

Gong-kuang zidonghua, 2023
The microseismic first arrival time picking is an important prerequisite for the high-precision positioning of the microseismic source. The traditional manual picking method is inefficient.
LI Tieniu, HU Binxin, LI Huakun, GENG Wencheng, HAO Pengcheng, JI Xubo, SUN Zengrong, ZHU Feng, ZHANG Hua, YANG Chengquan   +9 more
doaj   +1 more source

Exploring wave propagation behaviors in rock: A grain‐based perspective on mineral structures

Deep Underground Science and Engineering, EarlyView.
This study investigates wave propagation in rock at the grain scale using a grain‐based model, revealing that mineral elastic modulus significantly influences wave attenuation while grain size and distribution have limited effects. A novel peak particle velocity attenuation prediction model is proposed and validated for grain‐scale wave propagation ...
Zhiyi Liao, Jiachen Xu, Kai Zhang, Changtai Zhou   +3 more
wiley   +1 more source

Siamese Unsupervised Clustering For Removing Uncertainty In Microseismic Signal Labelling

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
The labelling of large seismic datasets is a challenging problem. Currently the methods most favoured by geoscientists are based on well known geophysical properties with STA/LTA ratio pickers remaining highly trusted to generate results which can be quickly attributed due to their ability to pick relatively high Signal to Noise Ratio (SNR) events with
Murray, David, Stankovic, Lina, Stankovic, Vladimir   +2 more
openaire   +3 more sources

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 Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

, 2020
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST ...
Xiang Li, Linlu Dong, Biao Li, Yifan Lei, N. Xu   +4 more
semanticscholar   +1 more source

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

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

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

Analysis and Optimization of Early‐Warning Indicators for Impact Critical Stress in Deep Soft Coal Seams

Energy Science &Engineering, EarlyView.
This study determines the impact critical stress early‐warning indicators for deep soft coal seams: 18 MPa (shallow holes), 20 MPa (deep holes), and 3 MPa/d daily stress change rate, via theoretical, experimental and simulation methods. ABSTRACT The accurate determination of early‐warning indicators for critical impact stress in deep soft coal seams is
Haichen Yin, Xiufeng Zhang, Atao Li, Yanliang Wang, Long Yuan, Chaohong Shi, Yang Chen, Caiping Lu, Liangjin Jiang   +8 more
wiley   +1 more source

Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning

International Journal of Computational Intelligence Systems, 2023
Microseismic signals contain various information for oil and gas developing. Increasing the signal-to-noise ratio of microseismic signals can successfully improve the effectiveness of oil and gas resource exploration.
Xuegui Li, Shuo Feng, Yuantao Guo, Hanyang Li, Yingjie Zhou   +4 more
doaj   +1 more source