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Least-Squares Migration: Data Domain Versus Image DomainNormal access

Authors: R.P. Fletcher, D. Nichols, R. Bloor and R.T. Coates
Event name: 77th EAGE Conference and Exhibition 2015
Session: Seismic Imaging Theory - New Imaging Directions
Publication date: 01 June 2015
DOI: 10.3997/2214-4609.201412941
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 933.4Kb )
Price: € 20

Conventional amplitude inversion assumes that the input migrated image has preserved relative amplitude information and is free from the effects of illumination. However, illumination effects caused by complex geological settings or by undersampled acquisition geometry and limited recording aperture pose a challenge to even the most advanced imaging algorithms. Additionally, standard depth migration images can suffer from lack of resolution caused by wavelet stretch effects and attenuation. Given a sufficiently accurate migration velocity model, least-squares migration (LSM) can mitigate many of these problems and produce better resolved migration images suitable for AVO inversion and extracting further information on lithology, reservoir quality and fluids. It can be formulated either in the data domain or the image domain. If implemented accurately, with the same estimates of noise statistics and the same operators, the two approaches should produce almost identical results when solving the same problem, albeit with differing costs. Practical considerations to reduce the cost of both approaches differ and this paper discusses the relative merits of both approaches. In a data-domain implementation, the convergence to localized problems can require a large numbers of iterations and may not easily resolve localized illumination variations that an image-domain implementation could handle. This paper advocates that, when a data-domain implementation of LSM is considered a necessary processing step, the image-domain implementation should be considered at the same time, especially when targeting localized reservoir targets under complex overburdens.

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