Results 151 to 160 of about 4,442 (191)

A Multispectral Denoising Framework for Seismic Random Noise Attenuation

IEEE Transactions on Geoscience and Remote Sensing, 2022
Random noise attenuation plays an important role in the seismic data processing. The global coherency among different spectral segments is often neglected in the traditional denoising methods, even though the seismic data are naturally broadband in the frequency spectrum.
Yi Lin, Jinhai Zhang
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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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Weighted Multisteps Adaptive Autoregression for Seismic Image Denoising

IEEE Geoscience and Remote Sensing Letters, 2018
We devised a new filtering technique for random noise attenuation by weighted multistep adaptive autoregression (WMAAR). We first obtain a series of denoised results by means of different steps adaptive AR, and then we sum these results with different weights.
Guochang Liu, Yang Liu, Chao Li 0016, Xiaohong Chen 0003   +3 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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Structured Graph Dictionary Learning and Application on the Seismic Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2019
Sparse coding method has been used for seismic denoising, as the data can be sparsely represented by the sparse transform and dictionary learning (DL) methods. DL methods have attracted wide attention because the learned dictionary is adaptive. However, for seismic denoising, the dictionary learned from the noise data is a mix of atoms representing ...
Lina Liu 0004, Jianwei Ma
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Seismic denoising based on modified BP neural network

2010 Sixth International Conference on Natural Computation, 2010
A new method for seismic random noise reduction based on robust function and back propagation (BP) neural network is proposed in this paper. This method introduces BP neural network utilizing least mean log squares (LMLS) error function or least trimmed squares (LTS) estimator instead of least mean squares (LMS) error function as its error function ...
Yinxue Zhang, Xuemin Tian, Xiaogang Deng, Yuping Cao   +3 more
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Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2020
Seismic interpretation is often limited by low resolution and strong noise data. To deal with this issue, we propose to leverage deep convolutional neural network (CNN) to achieve seismic image super-resolution and denoising simultaneously. To train the CNN, we simulate a lot of synthetic seismic images with different resolutions and noise levels to ...
Jintao Li, Xinming Wu, Zhanxuan Hu
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Seismic signal denoising method based on curvelet transform

2010 Sixth International Conference on Natural Computation, 2010
Considering the characteristic of curvelet coefficients in difference levels, a adaptive threshold denoising method is proposed by using fast discrete curvelet transform. Using total variation minimization reduces the noise while edges are preserved.
Aidi Wu, Xiuling Zhao
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Operational Seismic Denoising Workflow to Enhance Seismic Catalogues

Recent studies have demonstrated the potential of deep learning (DL) techniques for denoising seismic signals and improving signal analysis, but they are not yet widely adopted in seismic monitoring. Denoising models are typically applied to short segments of triggered data.
Nikolaj Dahmen, John Clinton, Men-Andrin Meier   +2 more
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Seismic Denoise Using Side Window Filter

EAGE 2020 Annual Conference & Exhibition Online, 2020
Summary Local windows are routinely used in seismic data and almost without exception the center of the window is aligned with the points being processed. When a point is on an edge, placing the center of the window on the point is one of the fundamental reasons that cause many filtering algorithms to blur the edges. Based on this insight, we use a new
D. Chang, G. Zhang, Y. Wang, J. Zhang
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