Results 61 to 70 of about 5,315,268 (243)

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

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

International Journal of Geophysics, 2021
In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location.
Xiaolong Guo
doaj   +1 more source

Classification of Microseismic Signals Using Machine Learning

Processes
The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural ...
Ziyang Chen, Yi Cui, Yuanyuan Pu, Yichao Rui, Jie Chen, Deren Mengli, Bin Yu   +6 more
openaire   +1 more source

DAMPING MEASUREMENTS FROM MICROSEISMIC SIGNALS TO INFER ROCK MASS DAMAGING

Italian Journal of Engineering Geology and Environment, 2019
Geophysical monitoring performed on unstable portions of rock masses can help in defining a variation in its stability conditions, resulting as a useful indicator for landslide risk reduction. In particular, understanding geophysical markers of rock mass damaging, intended as the processes that lead to the formation or to the growth of fractures, could
DANILO D’ANGIÒ, LUCA LENTI, SALVATORE MARTINO   +2 more
openaire   +2 more sources

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

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

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

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

Filtering of microseismic data based on information about signal phases

IOP Conference Series: Earth and Environmental Science, 2021
Abstract Seismic data filtering algorithm has been developed. The proposed algorithm allows amplifying signals from the sources located inside a selected area of space. The paper presents a theory describing the principle of this algorithm. Testing on synthetic data showed that the proposed filtering method is capable to suppress signals
AV Azarov, AS Serdyukov
openaire   +1 more source

The Single-Channel Microseismic Mine Signal Denoising Method and Application Based on Frequency Domain Singular Value Decomposition (FSVD)

Sustainability, 2023
The purpose of denoising microseismic mine signals (MMS) is to extract relevant signals from background interference, enabling their utilization in wave classification, identification, time analysis, location calculations, and detailed mining feature ...
Quanjie Zhu, Longkun Sui, Qingsong Li, Yage Li, L. Gu, Dacang Wang   +5 more
semanticscholar   +1 more source