Results 51 to 60 of about 615 (171)

Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning

International Journal of Computational Intelligence Systems, 2023
Microseismic signals contain various information for oil and gas developing. Increasing the signal-to-noise ratio of microseismic signals can successfully improve the effectiveness of oil and gas resource exploration.
Xuegui Li, Shuo Feng, Yuantao Guo, Hanyang Li, Yingjie Zhou   +4 more
doaj   +1 more source

Analysis and Optimization of Early‐Warning Indicators for Impact Critical Stress in Deep Soft Coal Seams

Energy Science &Engineering, Volume 14, Issue 8, Page 3726-3740, August 2026.
This study determines the impact critical stress early‐warning indicators for deep soft coal seams: 18 MPa (shallow holes), 20 MPa (deep holes), and 3 MPa/d daily stress change rate, via theoretical, experimental and simulation methods. ABSTRACT The accurate determination of early‐warning indicators for critical impact stress in deep soft coal seams is
Haichen Yin, Xiufeng Zhang, Atao Li, Yanliang Wang, Long Yuan, Chaohong Shi, Yang Chen, Caiping Lu, Liangjin Jiang   +8 more
wiley   +1 more source

Analysis of surface transient charge and the interdependent response of microseismic signal during the deformation of coal

矿业科学学报, 2020
The stress changes, transient charges and microseismic signals of briquette samples during uniaxial compression failure were monitored and analyzed by using the self-made experimental system for measuring transient charges and microseismic signals on the
Li Jing, Guan Cheng, Wang Zhen, Li Chengwu   +3 more
doaj   +1 more source

Time difference of arrival estimation of microseismic signals based on alpha-stable distribution [PDF]

Nonlinear Processes in Geophysics, 2018
Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follow α-stable distribution reveals that ...
R.-S. Jia, R.-S. Jia, Y. Gong, Y. Gong, Y.-J. Peng, Y.-J. Peng, H.-M. Sun, H.-M. Sun, X.-L. Zhang, X.-L. Zhang, X.-M. Lu, X.-M. Lu   +11 more
doaj   +1 more source

Research on the Critical Mechanical Criterion for Stick‐Slip Instability of Mining‐Affected Normal Faults Based on the Rate‐and‐State Friction Law

Energy Science &Engineering, Volume 14, Issue 8, Page 3935-3947, August 2026.
Mining disturbance tends to trigger fault stick‐slip instability and induce disasters. This paper establishes a mechanical model to propose a critical criterion, which is verified to be reliable via FLAC3D, providing a theoretical basis for early warning of fault slip disasters.
Bowen Wu, Yanhui Li
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

Transfer Learning and Benchmarking for Induced Seismic Event Detection: Insights From Oklahoma

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Machine learning models for microseismicity detection are often limited by the scarcity of large and high‐quality labeled data sets in many regions. To address this need, we introduce the Oklahoma Labeled AI Dataset (OKLAD), a manually curated data set compiled by the Oklahoma Geological Survey (OGS).
Hongyu Xiao, Jacob I. Walter, Paul Ogwari, Long M. Ho, Andrew D. Thiel, Nicholas Gregg, Brandon Mace, Isaac Woelfel   +7 more
wiley   +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 ...
Chen, Ziyang, Cui, Yi, Pu, Yuanyuan, Rui, Yichao, Chen, Jie, Mengli, Deren, Yu, Bin   +6 more
openaire   +1 more source

Surrogate‐Assisted Bayesian Inference of Fracture Network Parameters From Elastic Waves: A Sensitivity‐Guided Approach

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract We develop a sensitivity‐guided, surrogate‐assisted Bayesian framework to infer fracture network parameters from elastic waves. Synthetic fracture networks characterized by power‐law length exponent a $a$, fracture density d $d$, and percolation parameter p $p$ are constructed.
Le Zhang, Qinghua Lei, Longjun Dong, Chuanyin Jiang, Thomas Hermans   +4 more
wiley   +1 more source

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

IEEE Access, 2019
Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can ...
Ling-Qun Zuo, Hong-Mei Sun, Qi-Chao Mao, Xiao-Ying Liu, Rui-Sheng Jia   +4 more
doaj   +1 more source