Results 131 to 140 of about 672 (170)

Widely linear denoising of multicomponent seismic data

Geophysical Prospecting, 2019
ABSTRACTSeismic data processing is a challenging task, especially when dealing with vector‐valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components.
Breno Bahia, Mauricio D. Sacchi
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A Branch Construction-Based CNN Denoiser for Desert Seismic Data

IEEE Geoscience and Remote Sensing Letters, 2021
Seismic random noise reduction is an indispensable step in seismic data processing. Due to complex geological condition and acquisition environment, random noise in the desert seismic data has spatiotemporally variant noise levels and weak similarity to the signals, which severely obscures the seismic signals and increases the difficulty to extract the
Hongbo Lin, Shifu Wang, Yue Li 0003
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Interpolating and denoising of seismic data by randomized SVD

Global Meeting Abstracts, 2012
In this paper we present an algorithm which suppresses random noise and reconstructs the missing observations simultaneously. The method is based on rank reduction technique. In noiseless seismic records, k plane waves rank the Hankel matrix ”k” but, in the presence of noise and missing traces the Hankel matrix ranks more.
Farzaneh Bayati*, Hamid Reza Siahkoohi
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Unsupervised CNN Based on Self-Similarity for Seismic Data Denoising

IEEE Geoscience and Remote Sensing Letters, 2022
Convolutional neural network (CNN)-based methods are powerful tools for seismic data denoising. Most methods adopt a supervised learning strategy, which requires noise-free labels to construct an objective function to guide the training of network parameters; however, it is impossible to obtain true noise-free field data.
Wenqian Fang, Lihua Fu, Hongwei Li 0003
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Simultaneous dictionary learning and denoising for seismic data

Geophysics, 2014
ABSTRACT We evaluated a dictionary learning (DL) method for seismic-data denoising. The data were divided into smaller patches, and a dictionary of patch-size atoms was learned. The DL method offers a more flexible framework to adaptively construct sparse data representation according to the seismic data themselves. The representation
Simon Beckouche, Jianwei Ma
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Nonlocal Total Variation Denoising of Seismic Data

Proceedings, 2013
Seismic denoising can be considered to be a total variation minimization problem. Nonlocal total variation (NLTV) denoising is one of the best denoising models and is widely used in image processing. Combined with Split-Bregman algorithm, the computational efficiency of NLTV regularization can be improved, making it able to handle large data set.
S. Shang, L.G. Han
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A Novel Convolutive ICA for Seismic Data Denoising

2012
A main task of geophysical exploration is to remove random noises in seismic data processing to improve the SNR. Recently blind source separation (BSS) theory is applied to remove seismic random noises. But most are based on the instantaneous mixture model and limited to the synthetic seismic records.
Tian Yanan, Li Yue, Wang Bo, Liu Yanping, Zhong Tie   +4 more
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Application of Wavelet Analysis in Denoising Seismic Data

Applied Mechanics and Materials, 2014
The random noise is the kind of noise with wide frequency band in seismic data detected by the optical acceleration sensors. The noises influence and destroy the useful signal of the seismic information. There are a lot of methods to remove noise and one of the standard methods to remove the noise of the signal was the fast Fourier transform (FFT ...
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Damped Dreamlet Representation for Exploration Seismic Data Interpolation and Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2018
The dreamlet (drumbeat-beamlet) transform can provide us an efficient method to represent physical wavefield, because the dreamlet basis satisfies automatically the wave equation, which is a distinctive feature different from mathematical basis, such as Fourier and curvelet.
Weilin Huang, Ru-Shan Wu, Runqiu Wang
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Local quantum filtering and denoising of seismic data

Geophysics
ABSTRACT The precise characterization and analysis of complex geologic targets in seismic reflection data are posing increasingly high demands on the capabilities of signal processing methods. A recent contribution to signal processing is the Schrödinger equation-based adaptive transformation, which projects seismic data onto a ...
Ya-Juan Xue, Hong Zhang, Jin-Qiang Zhang, Xing-Jian Wang, Jun-Xing Cao, Zhe-Ge Liu   +5 more
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