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Research paper thumbnail of Random noise attenuation in seismic data using an adaptive thresholding and the second-order variant time-reassigned synchrosqueezing transform

Acta Geophysica, May 27, 2024

Research paper thumbnail of Time-Reassigned Synchrosqueezing Transform and low-frequency shadows associated with gas detection

Research paper thumbnail of Denoising of multidimensional seismic data in the physical domain by a new non-local self similarity method

Earth Science Informatics, Dec 23, 2022

Research paper thumbnail of Seismic low-frequency shadow detection based on the Levenberg-Marquardt reassignment operators using S-transforms

4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022)

Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much... more Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much theoretical research and practical production data. Therefore, accurate detection of the low-frequency shadow in order to predict reservoir. The high-precision detection time-frequency transform in this paper is achieved by adding Levenberg-Marquardt reassignment operators using S-transforms to adjust the window width adaptively according to the characteristics of different signal components. Finally, it optimizes the time-frequency distribution. Simulation results show that this method has a better time-frequency concentration than conventional methods. Finally, an application of this method in detecting low-frequency shadow verifies the effectiveness and feasibility, which provides a high-precision tool and means for reservoir prediction.

Research paper thumbnail of Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Research paper thumbnail of Random noise attenuation in 3D seismic data by iterative block tensor singular value thresholding

2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), 2017

The principal component analysis (PCA) is one of the most widely used technique in two-dimensiona... more The principal component analysis (PCA) is one of the most widely used technique in two-dimensional data analysis which uses singular value decomposition of matrix data and extracts its low-rank components. Using the PCA, seismic signals are represented in a sparse way which is a useful and popular methodology in signal-processing applications. Tensor principal component analysis (TPCA) as a multi-linear extension of principal component analysis, converts a set of correlated measurements into several principal components. In this paper, based on the singular value decomposition and extracting low-rank component as the denoised data, we used a new version of TPCA for denoising 3D seismic data in which, tensor data split into a number of blocks of the same size. The low-rank component of each block tensor is extracted using iterative tensor singular value thresholding method. The principal components of the multi-way data are the concatenation of all the low-rank components of all the block tensors. To demonstrate the performance of the proposed method for denoising 3D seismic data, we apply it to a 3D synthetic seismic data and a 3D real seismic data.

Research paper thumbnail of Random noise attenuation in seismic data using Hankel sparse low-rank approximation

Computers & Geosciences, 2021

Research paper thumbnail of Random noise attenuation of 2D seismic data based on sparse low-rank estimation of the seismic signal

Computers & Geosciences, 2020

Research paper thumbnail of Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019

Research paper thumbnail of Enhancing 3-D Seismic Data Using the t-SVD and Optimal Shrinkage of Singular Value

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018

Research paper thumbnail of Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

IEEE Transactions on Geoscience and Remote Sensing, 2017

Research paper thumbnail of Seismic low-frequency shadow detection based on the Levenberg-Marquardt reassignment operators using S-transforms

Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much... more Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much theoretical research and practical production data. Therefore, accurate detection of the low-frequency shadow in order to predict reservoir. The high-precision detection time-frequency transform in this paper is achieved by adding Levenberg-Marquardt reassignment operators using S-transforms to adjust the window width adaptively according to the characteristics of different signal components. Finally, it optimizes the time-frequency distribution. Simulation results show that this method has a better time-frequency concentration than conventional methods. Finally, an application of this method in detecting low-frequency shadow verifies the effectiveness and feasibility, which provides a high-precision tool and means for reservoir prediction.

Research paper thumbnail of Random noise attenuation in seismic data using an adaptive thresholding and the second-order variant time-reassigned synchrosqueezing transform

Acta Geophysica, May 27, 2024

Research paper thumbnail of Time-Reassigned Synchrosqueezing Transform and low-frequency shadows associated with gas detection

Research paper thumbnail of Denoising of multidimensional seismic data in the physical domain by a new non-local self similarity method

Earth Science Informatics, Dec 23, 2022

Research paper thumbnail of Seismic low-frequency shadow detection based on the Levenberg-Marquardt reassignment operators using S-transforms

4th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2022)

Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much... more Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much theoretical research and practical production data. Therefore, accurate detection of the low-frequency shadow in order to predict reservoir. The high-precision detection time-frequency transform in this paper is achieved by adding Levenberg-Marquardt reassignment operators using S-transforms to adjust the window width adaptively according to the characteristics of different signal components. Finally, it optimizes the time-frequency distribution. Simulation results show that this method has a better time-frequency concentration than conventional methods. Finally, an application of this method in detecting low-frequency shadow verifies the effectiveness and feasibility, which provides a high-precision tool and means for reservoir prediction.

Research paper thumbnail of Expand Dimensional of Seismic Data and Random Noise Attenuation Using Low-Rank Estimation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Research paper thumbnail of Random noise attenuation in 3D seismic data by iterative block tensor singular value thresholding

2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), 2017

The principal component analysis (PCA) is one of the most widely used technique in two-dimensiona... more The principal component analysis (PCA) is one of the most widely used technique in two-dimensional data analysis which uses singular value decomposition of matrix data and extracts its low-rank components. Using the PCA, seismic signals are represented in a sparse way which is a useful and popular methodology in signal-processing applications. Tensor principal component analysis (TPCA) as a multi-linear extension of principal component analysis, converts a set of correlated measurements into several principal components. In this paper, based on the singular value decomposition and extracting low-rank component as the denoised data, we used a new version of TPCA for denoising 3D seismic data in which, tensor data split into a number of blocks of the same size. The low-rank component of each block tensor is extracted using iterative tensor singular value thresholding method. The principal components of the multi-way data are the concatenation of all the low-rank components of all the block tensors. To demonstrate the performance of the proposed method for denoising 3D seismic data, we apply it to a 3D synthetic seismic data and a 3D real seismic data.

Research paper thumbnail of Random noise attenuation in seismic data using Hankel sparse low-rank approximation

Computers & Geosciences, 2021

Research paper thumbnail of Random noise attenuation of 2D seismic data based on sparse low-rank estimation of the seismic signal

Computers & Geosciences, 2020

Research paper thumbnail of Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019

Research paper thumbnail of Enhancing 3-D Seismic Data Using the t-SVD and Optimal Shrinkage of Singular Value

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018

Research paper thumbnail of Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

IEEE Transactions on Geoscience and Remote Sensing, 2017

Research paper thumbnail of Seismic low-frequency shadow detection based on the Levenberg-Marquardt reassignment operators using S-transforms

Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much... more Based on the phenomenon of low-frequency shadow beneath the oil and gas reservoirs, there is much theoretical research and practical production data. Therefore, accurate detection of the low-frequency shadow in order to predict reservoir. The high-precision detection time-frequency transform in this paper is achieved by adding Levenberg-Marquardt reassignment operators using S-transforms to adjust the window width adaptively according to the characteristics of different signal components. Finally, it optimizes the time-frequency distribution. Simulation results show that this method has a better time-frequency concentration than conventional methods. Finally, an application of this method in detecting low-frequency shadow verifies the effectiveness and feasibility, which provides a high-precision tool and means for reservoir prediction.

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