Unsupervised processing of geophysical signals: A review of some key aspects of blind deconvolution and blind source separation
This article reviews some key aspects of two important branches in unsupervised signal
processing: blind deconvolution and blind source separation (BSS). It also gives an …
processing: blind deconvolution and blind source separation (BSS). It also gives an …
Unsupervised deep learning for ground roll and scattered noise attenuation
The attenuation of coherent noise in land seismic data, specifically ground roll and near-
surface scattered energy, remains a longstanding challenge. Although recent advances in …
surface scattered energy, remains a longstanding challenge. Although recent advances in …
Ground-roll attenuation using generative adversarial networks
Y Yuan, X Si, Y Zheng - Geophysics, 2020 - library.seg.org
Ground roll is a persistent problem in land seismic data. This type of coherent noise often
contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data …
contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data …
A self‐supervised scheme for ground roll suppression
In recent years, self‐supervised procedures have advanced the field of seismic noise
attenuation, due to not requiring a massive amount of clean labelled data in the training …
attenuation, due to not requiring a massive amount of clean labelled data in the training …
Coherent noise suppression via a self-supervised deep learning scheme
Coherent noise attenuation is an essential step in seismic data processing to improve data
quality and signal-to-noise ratio. The use of deep learning based approaches for noise …
quality and signal-to-noise ratio. The use of deep learning based approaches for noise …
[HTML][HTML] 基于地震信号波形形态差异的面波噪声稀疏优化分离方法
陈文超， 王伟， 高静怀， 姜呈馥， 雷江莉 - 地球物理学报, 2013 - html.rhhz.net
实际地震信号通常可表示为具有波形特征差异的多种基本波形信号的线性组合,
如叠前道集中的工频干扰噪声与有效波信号, 面波噪声与体波信号等. 选择单一数学变换方法 …
如叠前道集中的工频干扰噪声与有效波信号, 面波噪声与体波信号等. 选择单一数学变换方法 …
Three-component high-resolution seismic time–frequency polarization filter
M Kazemnia Kakhki, A Mokhtari… - Geophysical Journal …, 2024 - academic.oup.com
The analysis of earthquake recordings from three-component instruments can be
challenging due to overlapping events. Time–frequency (TF) polarization methods are …
challenging due to overlapping events. Time–frequency (TF) polarization methods are …
Seismic characterization of the blue mountain geothermal field
K Gao, L Huang, T Cladouhos - Energies, 2023 - mdpi.com
Subsurface characterization is crucial for geothermal energy exploration and production. Yet
hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate …
hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate …
Should we have labels for deep learning ground roll attenuation?
Ground roll attenuation of land seismic data is still an outstanding and challenging problem.
Deep learning is a powerful tool for separating signal from noise. Recently, supervised …
Deep learning is a powerful tool for separating signal from noise. Recently, supervised …
A novel time-domain polarization filter based on a correlation matrix analysis
Polarization filters (PFs) are widely used for denoising seismic data. These filters are applied
in the fields of seismology, microseismic monitoring, vertical seismic profiling, and …
in the fields of seismology, microseismic monitoring, vertical seismic profiling, and …