Non-uniform optimal sampling for simultaneous source survey design
Bibliographic record
Abstract
Summary Optimal selection of locations for sensors in a seismic survey has been a long-standing issue for geophysicists. If we could sample the earth at two points per wavelength or better in all dimensions according to Nyquist sampling theory, design would not be an issue. The reality of limited access and funding requires us to make do with orders of magnitude fewer sampling points than Nyquist theory would dictate. The field of Compressive Sensing provides a new theory for non-uniform sampling that allows for using significantly fewer sensors than current practice in seismic exploration (Herrmann, 2010). We use these principles to define a pragmatic framework for seismic survey design, acquisition, processing and imaging, that we refer to as Compressive Seismic Imaging (CSI). In previous work we have described the CSI frameworks used for creating optimally sampled locations for sources and receivers that maximize our ability to recover the available bandwidth in seismic data. These same principles can be used to design surveys that use multiple simultaneous sources. In this paper we describe work flows for designing Non-Uniform Optimal Sampling (NUOS) locations for sources that maximize our ability to de-blend the data at high signal-to-noise ratios back to individual shot records. These work flows were used to design a blended dual-source survey that was shot immediately after the completion of a traditional single-source survey. Shooting time for the blended survey was reduced by more than half, with comparable or better data quality obtained for the blended source survey compared to the single source survey. After simple fast track processing, 4D differences between the blended and single source data were comparable to those obtained for 4D projects with similar geometries in nearby areas.
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How this classification was reachedexpand
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".