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- W4285229863 abstract "Summary In this work, we present a 3D structure adaptive anisotropic Gaussian kernel improved Laplace pyramid algorithm based on gradient structure tensor (GST), which not only on the premise of keeping original seismic signal details sufficient to remove those who seriously limits the underground geological structure and crack information extraction of random noise, but also the seismic data can be decomposed into multi-scale spatial frequency bands on the premise of protecting the high-frequency details. The calculated results of the gradient structure tensor provide information about the local orientation of the geological structure. In order to eliminate the influence of transverse variation of wave impedance in the spatial derivative of seismic data, the process is carried out at the instantaneous phase. Based on the local structural features, a gaussian kernel with confidence level is introduced to adjust the shape of the Gaussian kernel adaptively at different structural features. Combined with the Laplace pyramid sampling method, the seismic volume is decomposed into multi-scale seismic data while preserving the boundary denoising. The effectiveness of this method is verified by the application of actual seismic data." @default.
- W4285229863 created "2022-07-14" @default.
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- W4285229863 date "2022-01-01" @default.
- W4285229863 modified "2023-10-18" @default.
- W4285229863 title "Structure-Oriented Laplacian Pyramid Algorithm Based on 3D Structure Adaptive Anisotropic Gaussian Kernel" @default.
- W4285229863 doi "https://doi.org/10.3997/2214-4609.202210601" @default.
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