Deep Learning Velocity Model Building Using an Ensemble Regression Approach
Second International Meeting for Applied Geoscience & Energy(2022)
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 ...
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Three-Dimensional Initial Salt Body Building for Full-Waveform Inversion Assisted by Deep Learning.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023
被引用2
GEOPHYSICAL PROSPECTING 2024
被引用0
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest