Operational deployment of compressive sensing systems for seismic data acquisition
Bibliographic record
Abstract
Abstract Compressive sensing (CS) provides a new basis for sampling that can increase sampling efficiency for seismic data acquisition by an order of magnitude. A major challenge for this new technology is to show that theoretical increases in sampling efficiency can be translated to real efficiency gains in the field. Along with efficiency gains, data quality must be preserved in order to gain acceptance of a new acquisition technology. CS designs require solution of large optimization problems that are consistent with compressive sampling theory. We refer to our optimization framework for CS-based acquisition design and processing as compressive seismic imaging (CSI). We illustrate our CSI framework on example projects for ocean-bottom node, narrow-azimuth marine streamer, and land vibroseis acquisition. The ocean-bottom-node project was conducted in the UK North Sea during the difficult winter season. A CSI dual-source design was used to significantly reduce shooting time for this project. The project was completed on time, under budget, and with data quality that exceeded the quality of an overlapping uniformly sampled survey. The narrow-azimuth marine CSI survey project was acquired in offshore Australia for field development purposes. Nonuniform CSI sampling was used to increase sampling efficiency for both sources and cables, resulting in significant improvements in data quality and lateral resolution. The land vibroseis project was conducted on the North Slope of Alaska. In this case, the goal was to acquire a development survey of sufficient size within a short time window. Nonuniform CSI sampling was used to support the use of 10 or more vibrators shooting simultaneously, along with improving sampling efficiency for both sources and receivers. Compared to conventional designs, the CSI survey achieved an order of magnitude improvement in field acquisition efficiency and step-function improvements in data quality. These examples show that theoretical improvements in sampling efficiency from CS can make real and significant impacts on seismic data acquisition and processing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".