Results 31 to 40 of about 615 (171)

A method to estimate the microseismic magnitude based on vector scanning

地震科学进展, 2023
The magnitude of a seismic event can be estimated by measuring seismic energy, seismic moment, and relationship between the energy and seismic magnitude (M)，etc.
Yanjun Feng, Beiyuan Liang
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

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

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

Mine microseismic signal denoising method and application based on Adaboost_LSTM prediction

Meitan xuebao
Microseismic early warning is of great significance for ensuring mine safety, where a good denoising and accurate P-wave arrival picking of a microseismic signal is fundamental to the reliability of microseismic monitoring. By observing a large amount of
Xueyi SHANG, Yong CHEN, Jie CHEN, Linlin CHEN, Yuanyuan PU   +4 more
doaj   +1 more source

A Nonparametric Method for Automatic Denoising of Microseismic Data

Shock and Vibration, 2018
Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data.
Pingan Peng, Liguan Wang
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

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

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

Mine Microseismic Signal Denoising Based on a Deep Convolutional Autoencoder

Shock and Vibration, 2023
Mine microseismic signal denoising is a basic and crucial link in microseismic data processing, which influences the accuracy and reliability of the monitoring system, and is of great significance with regard to safety during mining. Therefore, this study introduces a deep learning method to improve the mapping function and sparsity of signals in the ...
Ting Hu, Bin Xu, Yongfa Wang, Jiayi Zhu, Jiang Zhou, Zhongyi Wan   +5 more
openaire   +2 more sources