The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
James H Mcclellan - One of the best experts on this subject based on the ideXlab platform.
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sparse promoting full waveform inversion based on online Orthonormal dictionary learning
Geophysics, 2017Co-Authors: Lingchen Zhu, Entao Liu, James H McclellanAbstract:ABSTRACTFull-waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between recorded and calculated seismic data. We have attacked this misfit successfully with the Gauss-Newton method and sparsity-promoting regularization based on fixed multiscale Transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical Transforms with predefined dictionaries to achieve sparse representation, we developed an adaptive Transform called the sparse Orthonormal Transform (SOT), whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be Orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations in the entire model perturbation. The complexity of the t...
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sparse promoting full waveform inversion based on online Orthonormal dictionary learning
arXiv: Geophysics, 2015Co-Authors: Lingchen Zhu, Entao Liu, James H McclellanAbstract:Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between the recorded and calculated seismic data. It has been attacked successfully with the Gauss-Newton method and sparsity promoting regularization based on fixed multiscale Transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical Transforms with predefined dictionaries to achieve sparse representation, we introduce an adaptive Transform called the Sparse Orthonormal Transform (SOT) whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be Orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations of the entire model perturbation. The complexity of the training method is proportional to the cube of the number of samples in one small patch. By incorporating both compressive subsampling and the adaptive SOT-based representation into the Gauss-Newton least-squares problem for each FWI iteration, the model perturbation can be recovered after an l1-norm sparsity constraint is applied on the SOT coefficients. Numerical experiments on synthetic models demonstrate that the SOT-based sparsity promoting regularization can provide robust FWI results with reduced computation.
Lingchen Zhu - One of the best experts on this subject based on the ideXlab platform.
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sparse promoting full waveform inversion based on online Orthonormal dictionary learning
Geophysics, 2017Co-Authors: Lingchen Zhu, Entao Liu, James H McclellanAbstract:ABSTRACTFull-waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between recorded and calculated seismic data. We have attacked this misfit successfully with the Gauss-Newton method and sparsity-promoting regularization based on fixed multiscale Transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical Transforms with predefined dictionaries to achieve sparse representation, we developed an adaptive Transform called the sparse Orthonormal Transform (SOT), whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be Orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations in the entire model perturbation. The complexity of the t...
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sparse promoting full waveform inversion based on online Orthonormal dictionary learning
arXiv: Geophysics, 2015Co-Authors: Lingchen Zhu, Entao Liu, James H McclellanAbstract:Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between the recorded and calculated seismic data. It has been attacked successfully with the Gauss-Newton method and sparsity promoting regularization based on fixed multiscale Transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical Transforms with predefined dictionaries to achieve sparse representation, we introduce an adaptive Transform called the Sparse Orthonormal Transform (SOT) whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be Orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations of the entire model perturbation. The complexity of the training method is proportional to the cube of the number of samples in one small patch. By incorporating both compressive subsampling and the adaptive SOT-based representation into the Gauss-Newton least-squares problem for each FWI iteration, the model perturbation can be recovered after an l1-norm sparsity constraint is applied on the SOT coefficients. Numerical experiments on synthetic models demonstrate that the SOT-based sparsity promoting regularization can provide robust FWI results with reduced computation.
Entao Liu - One of the best experts on this subject based on the ideXlab platform.
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sparse promoting full waveform inversion based on online Orthonormal dictionary learning
Geophysics, 2017Co-Authors: Lingchen Zhu, Entao Liu, James H McclellanAbstract:ABSTRACTFull-waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between recorded and calculated seismic data. We have attacked this misfit successfully with the Gauss-Newton method and sparsity-promoting regularization based on fixed multiscale Transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical Transforms with predefined dictionaries to achieve sparse representation, we developed an adaptive Transform called the sparse Orthonormal Transform (SOT), whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be Orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations in the entire model perturbation. The complexity of the t...
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sparse promoting full waveform inversion based on online Orthonormal dictionary learning
arXiv: Geophysics, 2015Co-Authors: Lingchen Zhu, Entao Liu, James H McclellanAbstract:Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between the recorded and calculated seismic data. It has been attacked successfully with the Gauss-Newton method and sparsity promoting regularization based on fixed multiscale Transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical Transforms with predefined dictionaries to achieve sparse representation, we introduce an adaptive Transform called the Sparse Orthonormal Transform (SOT) whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be Orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations of the entire model perturbation. The complexity of the training method is proportional to the cube of the number of samples in one small patch. By incorporating both compressive subsampling and the adaptive SOT-based representation into the Gauss-Newton least-squares problem for each FWI iteration, the model perturbation can be recovered after an l1-norm sparsity constraint is applied on the SOT coefficients. Numerical experiments on synthetic models demonstrate that the SOT-based sparsity promoting regularization can provide robust FWI results with reduced computation.
Hitoshi Kiya - One of the best experts on this subject based on the ideXlab platform.
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Avoidance of Singular Point in Integer Orthonormal Transform for Lossless Coding
IEEE Transactions on Signal Processing, 2012Co-Authors: Masahiro Iwahashi, Masanori Ogawa, Hitoshi KiyaAbstract:In this correspondence, the singular point (SP) problem peculiar to an integer Orthonormal Transform is discussed, and compatibility of the integer Transform with the corresponding real-valued Transform is improved. To avoid the SP problem, we introduce two-point permutations of order and sign of signals. Since it satisfies the commutative law, it becomes possible to reduce computational cost to find the best combination of the permutations which minimizes errors due to rounding of signals inside the integer Transform.
A T Walden - One of the best experts on this subject based on the ideXlab platform.
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multitaper power spectrum estimation and thresholding wavelet packets versus wavelets
IEEE Transactions on Signal Processing, 2002Co-Authors: A C Cristan, A T WaldenAbstract:It was suggested that spectrum estimation can be accomplished by applying wavelet denoising methodology to wavelet packet coefficients derived from the logarithm of a spectrum estimate. The particular algorithm we consider consists of computing the logarithm of the multitaper spectrum estimator, applying an Orthonormal Transform derived from a wavelet packet tree to the log multitaper spectrum ordinates, thresholding the empirical wavelet packet coefficients, and then inverting the Transform. For a small number of tapers, suitable Transforms/partitions for the logarithm of the multitaper spectrum estimator are derived using a method matched to statistical thresholding properties. The partitions thus derived starting from different stationary time series are all similar and easily derived, and any differences between the wavelet packet and discrete wavelet Transform (DWT) approaches are minimal. For a larger number of tapers, where the chosen parameters satisfy the conditions of a proven theorem, the simple DWT again emerges as appropriate. Hence, using our approach to thresholding and the method of partitioning, we conclude that the DWT approach is a very adequate wavelet-based approach and that the use of wavelet packets is unnecessary.
