Results 41 to 50 of about 1,776,592 (192)

Recognition of Microseismic and Blasting Signals in Mines Based on Convolutional Neural Network and Stockwell Transform

IEEE Access, 2020
The microseismic monitoring signals which need to be determined in mines include those caused by both rock bursts and by blasting. The blasting signals must be separated from the microseismic signals in order to extract the information needed for the ...
Guangdong Song, Jiulong Cheng, Kenneth T. V. Grattan   +2 more
doaj   +1 more source

Research on classification and identification of mine microseismic signals based on deep learning method

矿业科学学报, 2022
In order to accurately identify mine microseismic signals, this paper proposes a VGG4-CNN deep learning network model suitable for identifying mine microseismic signals. The model is written in Python language and built based on the PyTorch deep learning
Zhao Hongbao, Liu Rui, Liu Yihong, Zhang Yixiao, Gu Tao   +4 more
doaj   +1 more source

Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

, 2023
As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To
Jinmiao Wang, Pingan Peng, Ru Lei
core   +1 more source

Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10

Applied Sciences, 2023
Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams.
Yunpeng Zhang, Nan Li, Lihong Sun, Jincheng Qiu, Xiaokai Huang, Yan Li   +5 more
doaj   +1 more source

Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models

Applied Sciences
The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals.
Sihongren Shen, Bo Wang, Linfeng Zeng, Sheng Chen, Liujun Xie, Zilong She, Lanying Huang   +6 more
doaj   +1 more source

Research on Automatic Classification of Coal Mine Microseismic Events Based on Data Enhancement and FCN-LSTM Network

Applied Sciences, 2023
Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning.
Guojun Shang, Li Li, Liping Zhang, Xiaofei Liu, Dexing Li, Gan Qin, Hao Li   +6 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 Location Using Differential Backazimuth Angles [PDF]

, 2016
The most common procedure for locating borehole microseismic events is solution of a minimization problem based on observed travel time differences compared to calculated travel time differences derived from a given velocity model and oriented geophones.
Jacobsen, Rae
core  

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

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