Deep learning velocity model building using an ensemble regression approach

Second International Meeting for Applied Geoscience & Energy(2022)

Cited 2|Views4
No score
Abstract
PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyDeep learning velocity model building using an ensemble regression approachAuthors: Stuart FarrisGuillaume BarnierRobert ClappStuart FarrisStanford UniversitySearch for more papers by this author, Guillaume BarnierStanford UniversitySearch for more papers by this author, and Robert ClappStanford UniversitySearch for more papers by this authorhttps://doi.org/10.1190/image2022-w7-01.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFor full waveform inversion (FWI) to avoid local minima and converge to a useful velocity model, it must start from an initial model with accurate low wavenumber components. Unfortunately, the band-limited nature of seismic data makes extracting accurate low wavenumber information about the sub-surface extremely difficult, especially in geologic regimes with complex overburden such as salt or basalt formations. To overcome this problem, we propose a deep learning framework that uses a convolutional neural network (CNN) to form an ensemble of low wavenumber model predictions which can be integrated to form a starting model which is sufficient for FWI to avoid cycle skipping even in the presence of complex geology. We illustrate, on two synthetic benchmark datasets that contain complex salt, the ability of our deep learning ensemble approach to find sufficient starting models for FWI using only 4-8Hz band-limited, narrow azimuth streamer data.Keywords: full-waveform inversion, machine learningPermalink: https://doi.org/10.1190/image2022-w7-01.1FiguresReferencesRelatedDetails 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 Stuart Farris, Guillaume Barnier, and Robert Clapp, (2022), "Deep learning velocity model building using an ensemble regression approach," SEG Technical Program Expanded Abstracts : 3637-3641. https://doi.org/10.1190/image2022-w7-01.1 Plain-Language Summary Keywordsfull-waveform inversionmachine learningPDF DownloadLoading ...
More
Translated text
Key words
velocity model building,deep learning
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined