What is it about?
High-resolution seismic migration images are important for geophysical interpreters for characterizing the underground reserviors. However, the traditional least-squares migration method requires expensive computational and storage costs. We proposed a point spread function deconvolution method based on deep learning. Compared with the traditional least-squares migration method and the conventional deblurring filter method, our technique achives a better deconvolution result with reduced computational costs.
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Why is it important?
Our findings show that we could enhance the resolution of migration images with sufficiently reduced computational costs compared with the traditional least-squares reverse time migration method.
Read the Original
This page is a summary of: Deep learning-based point-spread function deconvolution for migration image deblurring, Geophysics, June 2022, Society of Exploration Geophysicists, DOI: 10.1190/geo2020-0904.1.
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