Deconvolution of the PSF of a seismic lens

Jianhua Yu; Yue Wang; Gerard T. Schuster

doi:10.1117/12.451797

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23 December 2002 Deconvolution of the PSF of a seismic lens

Jianhua Yu, Yue Wang, Gerard T. Schuster

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Proceedings Volume 4792, Image Reconstruction from Incomplete Data II; (2002) https://doi.org/10.1117/12.451797
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

We show that if seismic data d is related to the migration image by m_mig = L^Td. then m_mig is a blurred version of the actual reflectivity distribution m, i.e., m_mig = (L^TL)m. Here L is the acoustic forward modeling operator under the Born approximation where d = Lm. The blurring operator (L^TL), or point spread function, distorts the image because of defects in the seismic lens, i.e., small source-receiver recording aperture and irregular/coarse geophone-source spacing. These distortions can be partly suppressed by applying the deblurring operator (L^TL)^-1to the migration image to get m = (L^TL)^-1m_mig. This deblurred image is known as a least squares migration (LSM) image if (L^TL)^-1L^T is applied to the data d using a conjugate gradient method, and is known as a migration deconvolved (MD) image if (L^TL)^-1is directly applied to the migration image m_mig in (k_x, k_y, z) space. The MD algorithm is an order-of-magnitude faster than LSM, but it employs more restrictive assumptions. We also show that deblurring can be used to filter out coherent noise in the data such as multiple reflections. The procedure is to, e.g., decompose the forward modeling operator into both primary and multiple reflection operators d = (L_prim + L_multi)m, invert for m, and find the primary reflection data by d_prim = L_primm. This method is named least squares migration filtering (LSMF). The above three algorithms (LSM, MD and LSMF) might be useful for attacking problems in optical imaging.

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Jianhua Yu, Yue Wang, and Gerard T. Schuster "Deconvolution of the PSF of a seismic lens", Proc. SPIE 4792, Image Reconstruction from Incomplete Data II, (23 December 2002); https://doi.org/10.1117/12.451797

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