1887
Volume 41, Issue 3
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

A major underlying assumption of migration is that the input data are adequately sampled in terms of surface coverage. In addition, we hope that the subsurface is adequately illuminated, and that the migration algorithm itself is based on an acceptable numerical approximation of the wave equation. However, in general these assumptions and aspirations are never fully met, leading to amplitude imbalance and blurring of the output image. To some extent, this blurring and amplitude imbalance can be removed from the migrated data via application of some form of localised deconvolution, generally referred to as least-squares migration. This image modification can be performed in either the data or the image domain and can be achieved via an iterative or a single pass process, under the assumption that the velocity model is acceptable and that no coherent noise such as multiples, contaminates the input data. In this tutorial, we outline some of the various possible approaches to least-squares migration, and comment on the emerging ideas of adapting full waveform inversion to supplant some of the more expensive forms of least-squares image modification.

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2023-03-01
2024-03-29
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