Data-driven time-lapse acquisition design via optimal receiver-source placement and reconstruction
Bibliographic record
Abstract
We 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, 2020 Session Start Time: 8:30 AM Presentation Time: 10:35 AM Location: 362A Presentation Type: Oral
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".