Assessing the impact of program selection on the accuracy of 3D geologic models
Bibliographic record
Abstract
As the field of three-dimensional (3D) subsurface geological modeling develops at an increasingly rapid rate, so too does the number of available software programs catering to these applications, most of which offer very similar ensembles of algorithms for interpolating data. A few studies have analyzed the effect of algorithm selection on the accuracy and uncertainty of subsurface geologic models, but little consideration has been given to the uncertainty and variability introduced into the model by software program selection. In this study, inverse distance weighting (IDW) and ordinary kriging (OK) algorithms are used to interpolate identical data sets by three different software programs (ArcGIS, ROCKWORKS 2006, and VIEWLOG). The results indicate that the output of the IDW and OK interpolation algorithms are inconsistent between programs and that this variability should be considered when assessing the uncertainty associated with subsurface model results. Program selection appears to have a significant influence on model output results when modeling complex subsurface geological environments, particularly when interpolating clustered data, which are most commonly used in geological and environmental applications.
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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 | 0.051 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".