Enhancing the Reliability of 3D Subsurface Models through Differential Weighting and Mathematical Recombination of Variable Quality Data
Bibliographic record
Abstract
Abstract One of the first stages of the three‐dimensional (3D) subsurface modeling process involves collation and analysis of available borehole and/or outcrop data to identify individual subsurface units, usually distinguished by the grain size of the sediment, and the elevation of their bounding contacts. Input data can come from a variety of sources and may be categorized according to their reliability and/or quality. The output from the 3D model is a prediction of subsurface conditions based on these data and the reliability of the output model is highly dependent on both the quality of input data and the types of interpolation methods used. This article presents a new quality weighting methodology that allows the user to assign a differential weighting factor to data points of variable quality in the modeling process. Input data are categorized into high and low quality datasets which are then recombined using a grid math process in which a differential “weighting” factor is applied. This allows the 3D modeling program to maximize the use and effectiveness of data from all available sources while giving high quality data greater influence on the final model output, and will result in the generation of more accurate and reliable 3D subsurface models.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".