Results 1 to 10 of about 5,383,519 (211)
Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
Applied Sciences (Switzerland), 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.
Hongbo Li, Shunlei Hu
exaly +6 more sources
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.
Yingchun Li, Qiang Li, Qingyuan He
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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
exaly +5 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.
Wenjin Yan +7 more
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CNN-Transformer for Microseismic Signal Classification
Electronics (Switzerland), 2023 The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar.
Xingli Zhang +2 more
exaly +4 more sources
Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder–Decoder Network [PDF]
Applied Sciences (Switzerland), 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 +2 more
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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.
Yongfa Wang +5 more
core +4 more sources
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
Energies, 2021 It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems.
Sungil Kim, Kim Sungil
exaly +3 more sources
Deep convolutional neural network for microseismic signal detection and classification
Pure and Applied Geophysics, 2020 Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network
Pazzi V. +4 more
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Applied Sciences (Switzerland), 2023 Microseismic event identification is of great significance for enhancing our understanding of underground phenomena and ensuring geological safety. This paper employs a literature review approach to summarize the research progress on microseismic signal ...
Hongmei Shu, Ahmad Yahya Dawod
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