Enhance 3D seismic images resolution by deconvolving point spread function

Liu, Cewen; Fu, Haohuan

doi:10.1190/segam2021-3594599.1

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…In order to enhance the resolution of seismic images using PSF, it is crucial to generate an appropriate PSF distribution of the subsurface model. Various methods have been proposed for generating PSFs, including ray‐based (S. Liu et al., 2021; Xu et al., 2022; Yang et al., 2022), wave‐equation based (Jensen et al., 2021; Toxopeus et al., 2008) and demigration–migration methods (Aoki & Schuster, 2009; Fletcher et al., 2012; C. Liu & Fu, 2021). Ray‐based methods are computationally efficient but suffer from low accuracy, while wave‐equation based methods are computationally expensive to simulate the wave propagation process.…”

Section: Theory and Methodsmentioning

confidence: 99%

Enhancing the resolution of three‐dimensional migration images based on space‐variant point spread function deconvolution

Liu,

Sun,

et al. 2024

Geophysical Prospecting

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Improving the resolution of seismic migration images plays an important role for geophysical interpreters to characterize underground reservoirs. However, the classical image domain least‐squares migration method based on the local‐stationary assumption cannot obtain a satisfactory high‐resolution seismic image due to the significant spatial variant characteristics of the point spread function. To mitigate this problem, we proposed a high‐resolution point spread function deconvolution method and applied it to two‐dimensional cases. Nevertheless, extending the two‐dimensional method to three‐dimensional problems directly would fail due to the intrinsic complexity in three‐dimensional cases. In this study, we resolve the differences encountered in the point spread function deconvolution method for two‐ and three‐dimensional cases and provide specific strategies for achieving high‐resolution imaging with low computational cost when extending the point spread function deconvolution method to three‐dimensional cases. The main schemes include (1) incorporating the analytical expression of the point spread function to guide the generation of three‐dimensional point spread function distributions, (2) extending the point spread function filter calculation method from a two‐dimensional square to a three‐dimensional rectangular prism and (3) interpolating to obtain more compact point spread functions for reducing migration artefacts. Results from the three‐dimensional synthetic Overthrust model and field data set demonstrate that our techniques could effectively enhance the spatial resolution of the migration images with reduced migration artefacts. With these specific strategies, the space‐variant point spread function deconvolution algorithm shows superior performance on three‐dimensional cases at a much lower computational cost compared with the classical least‐squares migration method and the local‐stationary deblurring method. Synthetic tests and real data applications confirm that the space‐variant point spread function deconvolution method has distinct advantages over both two‐ and three‐dimensional problems and can be widely adopted in practice.

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Section: Theory and Methodsmentioning

confidence: 99%

Enhancing the resolution of three‐dimensional migration images based on space‐variant point spread function deconvolution

Liu,

Sun,

et al. 2024

Geophysical Prospecting

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Iterative Reweighted Least-Squares Gaussian Beam Migration and Velocity Inversion in the Image Domain Based on Point Spread Functions

Duan

Mao

Shi

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Image-domain seismic inversion by deblurring with invertible recurrent inference machines

Peng,

Vasconcelos,

Ravasi

2024

GEOPHYSICS

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In complex geological settings and in the presence of sparse acquisition systems, seismic migration images manifest as non-stationary blurred versions of the unknown subsurface model. Thus, image-domain deblurring is an important step to produce interpretable and high-resolution models of the subsurface. Most deblurring methods focus on inverting seismic images for their underlying reflectivity by iterative least-squares inversion of a local Hessian approximation; this is obtained by either direct modeling of the so-called point spread functions or by a migration-demigration process. In this work, we adopt a novel deep learning framework, based on invertible Recurrent Inference Machines (i-RIMs), which allows approaching any inverse problem as a supervised learning task informed by the known modeling operator (convolution with point-spread functions in our case): the proposed algorithm can directly invert migrated images for impedance perturbation models, assisted with the prior information of a smooth velocity model and the modeling operator. Because i-RIMs are constrained by the forward operator, they implicitly learn to shape/regularise output models in a training-data-driven fashion. As such, the resulting deblurred images show great robustness to noise in the data and spectral deficiencies (e.g., due to limited acquisition). The key role played by the i-RIM network design and the inclusion of the forward operator in the training process is supported by several synthetic examples. Finally, using field data, we show that i-RIM-based deblurring has great potential in yielding robust, high-quality relative impedance estimates from migrated seismic images. Our approach could be of importance towards future Deep-Learning-based quantitative reservoir characterization and monitoring.

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Enhance 3D seismic images resolution by deconvolving point spread function

Cited by 3 publications

References 11 publications

Enhancing the resolution of three‐dimensional migration images based on space‐variant point spread function deconvolution

Enhancing the resolution of three‐dimensional migration images based on space‐variant point spread function deconvolution

Iterative Reweighted Least-Squares Gaussian Beam Migration and Velocity Inversion in the Image Domain Based on Point Spread Functions

Image-domain seismic inversion by deblurring with invertible recurrent inference machines

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