Results 131 to 140 of about 612 (172)
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A Convolutional Autoencoder Method for Simultaneous Seismic Data Reconstruction and Denoising
IEEE Geoscience and Remote Sensing Letters, 2022Petroleum geophysical exploration is based on seismic data and has been widely affected by deep learning technology in recent years. As a consequence of the high efficiency and nonlinear fitting ability of deep learning models, we propose an improved convolutional autoencoder (CAE) method to achieve simultaneous reconstruction and denoising of seismic ...
Jinsheng Jiang, Haoran Ren, Meng Zhang
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Widely linear denoising of multicomponent seismic data
Geophysical Prospecting, 2019ABSTRACTSeismic 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, 2021Seismic 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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Nonlocal Total Variation Denoising of Seismic Data
Proceedings, 2013Seismic 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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Interpolating and denoising of seismic data by randomized SVD
Global Meeting Abstracts, 2012In 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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Simultaneous dictionary learning and denoising for seismic data
Geophysics, 2014ABSTRACT 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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Application of Wavelet Analysis in Denoising Seismic Data
Applied Mechanics and Materials, 2014The 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, 2018The 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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Mask-Guided Model for Seismic Data Denoising
IEEE Geoscience and Remote Sensing Letters, 2022Ziyi Fang, Hongbo Lin, Xinyu Xu
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A Novel Convolutive ICA for Seismic Data Denoising
2012A 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 +4 more
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