Land Surface-Related Multiple Attenuation Based on Wave-Equation Deconvolution
Second International Meeting for Applied Geoscience & Energy（2022）
CGG
Abstract
PreviousNext You have accessSecond International Meeting for Applied Geoscience & EnergyLand surface-related multiple attenuation based on wave-equation deconvolutionAuthors: Gordon PooleBrandon LiMattia MioraliKeith MillsJohn TickleGordon PooleCGGSearch for more papers by this author, Brandon LiCGGSearch for more papers by this author, Mattia MioraliCGGSearch for more papers by this author, Keith MillsCGGSearch for more papers by this author, and John TickleCGGSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3744945.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFree-surface multiple prediction of land data is challenging due to the unknown downward reflecting free-surface combined with poorly sampled recording of the shallow multiple generators. We use a wave-equation deconvolution approach to derive an image of the shallow multiple generators using the multiple periodicity. The image is then used to model multiples which are subsequently subtracted from the input data. The approach adjusts to variations in the shallow subsurface and the multiple model requires minimal adaptation.Keywords: demultiple, land, deconvolutionPermalink: https://doi.org/10.1190/image2022-3744945.1FiguresReferencesRelatedDetails 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 Gordon Poole, Brandon Li, Mattia Miorali, Keith Mills, and John Tickle, (2022), "Land surface-related multiple attenuation based on wave-equation deconvolution," SEG Technical Program Expanded Abstracts : 2376-2378. https://doi.org/10.1190/image2022-3744945.1 Plain-Language Summary KeywordsdemultiplelanddeconvolutionPDF 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
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
Summary is being generated by the instructions you defined