A new methodology using seismic data to differentiate shallow-water carbonate deposits from similar, noncarbonate structures
Bibliographic record
Abstract
ABSTRACT We present here a new methodology for helping to recognize shallow-water carbonate deposits in seismic data, implementing a quantitative tool, developed in a spreadsheet format, or table, that allows us to score shallow-water carbonate deposits against six specific types of similar, noncarbonate structures. We can score 15 parameters for each structure type. The table contains a description for each combination of structure type and parameter. The final result helps to distinguish noncarbonate structures from shallow-water carbonate deposits. Noncarbonate structures include arc volcanoes, other kinds of volcanoes, mobilized salt, shale, and mud structures, compressional tectonic features, basement highs, and erosional remnants. We have grouped diagnostic parameters in four categories, which include morphology, regional scale parameters, seismic response, and growth patterns. Testing, performed on 214 internal, multiclient, and published images from different ages and locations, has shown a positive match in 90% of the cases, 6% as negative matches, and 4% as uncertain results. The addition of new assessment criteria to previously published indicators, the strategy of scoring noncarbonate structures along with shallow-water carbonate deposits, and the attempt of building this methodology for the use of nonspecialist geoscientists are the main elements of novelty with respect to previous assessment techniques.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".