Interactive inverse methodology applied to stratigraphic forward modelling
Bibliographic record
Abstract
Abstract An effective inverse scheme that can be applied to complex 3-D hydrodynamic forward models has so far proved elusive. In this paper we investigate an interactive inverse methodology that may offer a possible way forward. The scheme builds on previous work in linking expert review of alternate output to rapid modification of input variables. This was tested using the SEDSIM 3-D stratigraphic forward-modelling program, varying nine input variables in a synthetic example. Ten SEDSIM simulations were generated, with subtle differences in input, and five dip sections (fences) were displayed for each simulation. A geoscientist ranked the lithological distribution in order of similarity to the true sections (the true input values were not disclosed during the experiment). The two or three highest ranked simulations then acted as seed for the next round of ten simulations, which were compared in turn. After 90 simulations a satisfactory match between the target and the model was found and the experiment was terminated. Subsequent analysis showed that the estimated input values were ‘close’ to the true values.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".