A recipe for practical iterative LSRTM with synthetic and real data examples from Brazil

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyA recipe for practical iterative LSRTM with synthetic and real data examples from BrazilAuthors: Valeriy BrytikGopal PalacharlaRishi BansalDiwi SnyderXu LiYoung Ho ChaPartha RouthInma Dura-GomezDmitriy PavlovCarey MarcinkovichValeriy BrytikExxonMobilSearch for more papers by this author, Gopal PalacharlaExxonMobilSearch for more papers by this author, Rishi BansalExxonMobilSearch for more papers by this author, Diwi SnyderExxonMobilSearch for more papers by this author, Xu LiExxonMobilSearch for more papers by this author, Young Ho ChaExxonMobilSearch for more papers by this author, Partha RouthExxonMobilSearch for more papers by this author, Inma Dura-GomezExxonMobilSearch for more papers by this author, Dmitriy PavlovExxonMobilSearch for more papers by this author, and Carey MarcinkovichExxonMobilSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751103.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractInversion-based imaging approaches such as LSRTM (Tarantola 1987; Claerbout 1992; Nemeth et al. 1999; Schuster 2017) have become an important part of seismic processing, especially in complex geologic regions where both high fidelity structural imaging and amplitudes are required. However, owing to the high computational cost of the forward modeling and gradient estimation steps, and the iterative nature of the approach, application of LSRTM can be expensive. In this abstract we discuss several improvements to a standard iterative LSRTM workflow, which can speed-up convergence, aid in the recovery of physically meaningful properties such as impedance, and overall reduce the cost of application. The improvements include wavelet selection, smart preconditioning via NMF (non-stationary matching filter), inversion parameter selection, multi-domain analytic line search, use of an alternate migration operator and shot selection. These workflow improvements have allowed us to significantly reduce the cost of applying LSRTM, while still generating high fidelity and physically meaningful results.Keywords: LSRTM, optimization, shaping, nonstationary filter, iterationPermalink: https://doi.org/10.1190/image2022-3751103.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Valeriy Brytik, Gopal Palacharla, Rishi Bansal, Diwi Snyder, Xu Li, Young Ho Cha, Partha Routh, Inma Dura-Gomez, Dmitriy Pavlov, and Carey Marcinkovich, (2022), "A recipe for practical iterative LSRTM with synthetic and real data examples from Brazil," SEG Technical Program Expanded Abstracts : 2689-2693. https://doi.org/10.1190/image2022-3751103.1 Plain-Language Summary KeywordsLSRTMoptimizationshapingnonstationary filteriterationPDF DownloadLoading ...