Results 1 to 10 of about 1,776,592 (192)

Mine-Microseismic-Signal Recognition Based on LMD–PNN Method

Applied Sciences, 2022
The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing.
Qiang Li, Yingchun Li, Qingyuan He
doaj   +5 more sources

Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues

Applied Sciences, 2023
The microseismic signals of rock fractures indicate that the rock mass in a particular area is changing slowly, and the microseismic signals of rock blasting indicate that the rock mass in a particular area is changing violently.
Junzhi Chen, Hongbo Li, Chunfang Ren, Fan Hu   +3 more
doaj   +5 more sources

An Improved Microseismic Signal Denoising Method of Rock Failure for Deeply Buried Energy Exploration

Energies, 2023
Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results.
Shibin Tang, Shun Ding, Jiaming Li, Chun Zhu, Leyu Cao   +4 more
doaj   +4 more sources

Parallel Processing Method for Microseismic Signal Based on Deep Neural Network

Remote Sensing, 2023
The microseismic signals released by rock mass fracture can be captured via microseismic monitoring to evaluate the development of geological disasters.
Chunchi Ma, Wenjin Yan, Weihao Xu, Tianbin Li, Xuefeng Ran, Jiangjun Wan, Ke Tong, Yu Lin   +7 more
doaj   +4 more sources

Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network [PDF]

Applied Sciences, 2020
Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses.
Hang Zhang, Chunchi Ma, Veronica Pazzi, Yulin Zou, Nicola Casagli   +4 more
doaj   +6 more sources

Distributed Compressed Sensing of Microseismic Signals Through First Break Time Extraction and Signal Alignment

IEEE Access, 2018
Microseismic monitoring is widely applied in dams, mines, and various fields of underground engineering. The number of sensors in microseismic monitoring systems is usually very large, which will result in a huge amount of data being produced if the ...
Ran Zhang, Qingsong Hu, Gang Wang, Bin Ye   +3 more
doaj   +4 more sources

Research on automatic segmentation method of microseismic monitoring signal under low signal-to-noise ratio conditions. [PDF]

PLoS One
To address the issues of reduced microseismic signal detection accuracy and difficulty in identifying event boundaries under low signal-to-noise ratio conditions, we propose SEU-Net, an automatic microseismic signal segmentation model that integrates a ...
Liu W, Li J, Zhang Z, Yuan Z, Teng S, Song D, Zhu M, Lv W.   +7 more
europepmc   +2 more sources

Swin Transformer Based Recognition for Hydraulic Fracturing Microseismic Signals from Coal Seam Roof with Ultra Large Mining Height. [PDF]

Sensors (Basel)
Accurate differentiation between microseismic signals induced by hydraulic fracturing and those from roof fracturing is vital for optimizing fracturing efficiency, assessing roof stability, and mitigating mining-induced hazards in coal mining operations.
Wang P, Feng Y, Sun X, Cheng X.
europepmc   +2 more sources

Research on computational propagation and identification of mine microseismic signals based on deep learning. [PDF]

PLoS One
In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion.
Liu D, Zhang J, Zhao B, Gao L, Zhou H, Cheng Z, Chen L, Li M.   +7 more
europepmc   +2 more sources

Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network

IEEE Access
During the downhole microseismic monitoring for hydraulic fracturing, microseismic signals are constantly vulnerable to interference from different kinds of noise.
Wenxuan Ge, Qinghui Mao, Wei Zhou, Zhixian Gui, Peng Wang   +4 more
doaj   +3 more sources