Source estimation with surface-related multiples—fast ambiguity-resolved seismic imaging. Issue 3 (14th March 2016)

• Record Type: Journal Article
• Authors: Tu, Ning; Aravkin, Aleksandr; van Leeuwen, Tristan; Lin, Tim; Herrmann, Felix J.
• Is Part Of: Geophysical journal international. Volume 205:Issue 3(2016:Jun.)
• Page Start: 1492
• Page End: 1511
• Publication Date: 2016-03-14
• Subjects: Numerical solutions -- Inverse theory -- Wave propagation; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggw097
• Languages: English
• ISSNs: 0956-540X

Abstract

Abstract: We address the problem of obtaining a reliable seismic image without prior knowledge of the source wavelet, especially from data that contain strong surface-related multiples. Conventional reverse-time migration requires prior knowledge of the source wavelet, which is either technically or computationally challenging to accurately determine; inaccurate estimates of the source wavelet can result in seriously degraded reverse-time migrated images, and therefore wrong geological interpretations. To solve this problem, we present a 'wavelet-free' imaging procedure that simultaneously inverts for the source wavelet and the seismic image, by tightly integrating source estimation into a fast least-squares imaging framework, namely compressive imaging, given a reasonably accurate background velocity model. However, this joint inversion problem is difficult to solve as it is plagued with local minima and the ambiguity with respect to amplitude scalings because of the multiplicative, and therefore nonlinear, appearance of the source wavelet in the otherwise linear formalism. We have found a way to solve this nonlinear joint-inversion problem using a technique called variable projection, and a way to overcome the scaling ambiguity by including surface-related multiples in our imaging procedure following recent developments in surface-related multiple prediction by sparse inversion. As a result, we obtain without prior knowledge of the source wavelet high-resolution seismic images, comparable in quality to images obtained assuming the true source wavelet is known. By leveraging the computationally efficient compressive-imaging methodology, these results are obtained at affordable computational costs compared with conventional processing work flows that include surface-related multiple removal and reverse-time migration.