Results 141 to 150 of about 4,173 (187)

Self-Supervised Learning for Seismic Data Reconstruction and Denoising

IEEE Geoscience and Remote Sensing Letters, 2022
With their powerful feature extraction ability, convolutional neural network (CNN) models achieve excellent signal reconstruction and recovery performances compared with those of traditional methods. The CNN-based approaches mainly use supervised learning approaches; thus, they require large numbers of ground-truth labeled samples.
Fanlei Meng, Qinyin Fan, Yue Li 0003
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Unsupervised Seismic Data Denoising Using Diffusion Denoising Model

IEEE Transactions on Geoscience and Remote Sensing
Fuyao Sun, HongBo Lin, Yue Li
exaly   +2 more sources

Curvelet Transform and its Application in Seismic Data Denoising

2009 International Conference on Information Technology and Computer Science, 2009
Curvelet transform is a new multi-scale transform developed upon wavelet transform. Beside scale and position, its constructive factors still include directions. All these make curvelet transform have a better directional characteristic. Based on these properties, we transform seismic data into curvelet domain, apply a window-shrinking algorithm to ...
Junhua Zhang, Xiaoteng Liang, Jinrong Fu, Jianle Guo, Xugang Zheng   +4 more
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Adaptive Dictionary Learning for Blind Seismic Data Denoising

IEEE Geoscience and Remote Sensing Letters, 2020
The data-driven tight frame (DDTF) method is a dictionary learning method which has been used widely in the adaptive sparse representation and the seismic random noise attenuation. In the DDTF method, the thresholding operator setting plays a significant role on balancing the noise removal and preservation of detail information with high frequency. The
Xiaojing Wang, Jianwei Ma
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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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A Convolutional Autoencoder Method for Simultaneous Seismic Data Reconstruction and Denoising

IEEE Geoscience and Remote Sensing Letters, 2022
Petroleum 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, 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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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
openaire   +1 more source

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
openaire   +1 more source