Data-driven time-lapse acquisition design via optimal receiver-source placement and reconstruction
Bibliographic record
Abstract
PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Data-driven time-lapse acquisition design via optimal receiver-source placement and reconstructionAuthors: Yi GuoMauricio D. SacchiYi GuoUniversity of Alberta and Mauricio D. SacchiUniversity of Albertahttps://doi.org/10.1190/segam2020-3426928.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe investigate a recently proposed method for optimal sensor placement that we have adapted for a time-lapse seismic acquisition application. The premise of our paper is that a dense acquisition conducted for the base survey can be used to design an optimal sparse acquisition geometry for the monitor survey. The method uses Proper Orthogonal Decomposition (POD) to extract the basis from the training dataset. The base survey provides the training dataset to estimate the POD. Instead of using a conventional universal basis, the POD basis extracted from the base survey provides a data representation that is particularly tailored to seismic data reconstruction. We determine the optimal acquisition geometry for the monitor survey via dimensionality reduction and QR factorization with column pivoting. Pivoting permits to determine the optimal source and receiver geometry for the monitor survey. Once the optimal geometry is estimated, the least-squares fitting of the POD basis is used for reconstructing the monitor survey. We also provide a comparison between random sampling followed by reconstruction and the proposed method.Presentation Date: Tuesday, October 13, 2020Session Start Time: 8:30 AMPresentation Time: 10:35 AMLocation: 362APresentation Type: OralKeywords: acquisition, reconstruction, sensors, time-lapse, survey designPermalink: https://doi.org/10.1190/segam2020-3426928.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 25 Sep 2020 CITATION INFORMATION Yi Guo and Mauricio D. Sacchi, (2020), "Data-driven time-lapse acquisition design via optimal receiver-source placement and reconstruction," SEG Technical Program Expanded Abstracts : 66-70. https://doi.org/10.1190/segam2020-3426928.1 Plain-Language Summary Keywordsacquisitionreconstructionsensorstime-lapsesurvey designPDF DownloadLoading ...
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".