Dynamic-warping full-waveform inversion to overcome cycle skipping (2016)

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2016Dynamic-warping full-waveform inversion to overcome cycle skippingAuthors: Min WangYi XieWei Quan XuFong Cheen LohKefeng XinBoon Leng ChuahTed ManningSimon WolfarthMin WangCGG, Yi XieCGG, Wei Quan XuCGG, Fong Cheen LohCGG, Kefeng XinCGG, Boon Leng ChuahCGG, Ted ManningBP, and Simon WolfarthBPhttps://doi.org/10.1190/segam2016-13855951.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract In this paper we develop dynamic-warping full-waveform inversion (D-FWI) to address the well-known cycle skipping problem in conventional full-waveform inversion (FWI). The dynamic warping technique is used to detect the traveltime difference between the predicted and the observed data. We make use of the timeshift to partially warp the observed data and thus generate a series of datasets that connect the predicted and the observed data. We then use the modified observed data to solve a sequence of conventional FWIs that avoid the cycle skipping issue. Synthetic and real data examples show that D-FWI can converge successfully by overcoming the cycle skipping problem, while conventional FWI results in an erroneous model. With this new approach, we can invert velocity models starting from a higher frequency and/or a poor starting model. This technology has the potential to save time in the processing sequence since it allows the velocity model building to start with minimum preprocessing on the seismic data and to be done in parallel with other pre-processing steps. Presentation Date: Wednesday, October 19, 2016 Start Time: 8:50:00 AM Location: 162/164 Presentation Type: ORAL Keywords: full-waveform inversion, high-resolution, inversion, velocityPermalink: https://doi.org/10.1190/segam2016-13855951.1FiguresReferencesRelatedDetailsCited byBuilding a full-waveform inversion starting model from wells with dynamic time warping and convolutional neural networksJiashun Yao and Yanghua Wang24 January 2022 | GEOPHYSICS, Vol. 87, No. 2FWI in extended domain using time-warpingGuanghui Huang, Jaime Ramos-Martínez, Yang Yang, and Nizar Chemingui1 September 2021Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope23 April 2021 | Pure and Applied Geophysics, Vol. 178, No. 5A review on reflection-waveform inversion18 March 2020 | Petroleum Science, Vol. 17, No. 2Convolution Coding and Amplitude Attenuation-Based Full Waveform InversionIEEE Access, Vol. 8Imaging challenges at Gulf of Papua (PNG) and their solutions through high-end imaging technology11 November 2019 | ASEG Extended Abstracts, Vol. 2019, No. 1Tackling cycle-skipping in full-waveform inversion with intermediate dataGang Yao, Nuno V. da Silva, Michael Warner, Di Wu, and Chenhao Yang10 August 2019Tackling cycle skipping in full-waveform inversion with intermediate dataGang Yao, Nuno V. da Silva, Michael Warner, Di Wu, and Chenhao Yang10 April 2019 | GEOPHYSICS, Vol. 84, No. 3Impact of Seismic Acquisition and Processing Techniques in Challenging Subsurface Imaging; Block G11/48, Gulf of Thailand12 November 2018 SEG Technical Program Expanded Abstracts 2016ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2016 Pages: 5654 publication data© 2016 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 01 Sep 2016 CITATION INFORMATION Min Wang, Yi Xie, Wei Quan Xu, Fong Cheen Loh, Kefeng Xin, Boon Leng Chuah, Ted Manning, and Simon Wolfarth, (2016), "Dynamic-warping full-waveform inversion to overcome cycle skipping," SEG Technical Program Expanded Abstracts : 1273-1277. https://doi.org/10.1190/segam2016-13855951.1 Plain-Language Summary Keywordsfull-waveform inversionhigh-resolutioninversionvelocityPDF DownloadLoading ...