Case studies in integrated geological and geophysical 3D modelling: Value added to exploration and mining projects
Bibliographic record
Abstract
The integration of geophysical with litho-structural models represents a valuable tool for better understanding of subsurface geometries of lithological contacts. Improved subsurface models add value to mineral exploration projects. Geophysical data is used to enhance and validate litho-structural models. The regular distribution of geophysical data allows lithologies and faults to be extended from observed locations into the subsurface. Geological models are validated and improved by comparison of the geophysical signal calculated from the model geology with the observed signal. Discrepancies between modeled and observed signals highlight areas requiring refinements of the geological model.The case studies present examples of how iterative modeling from geological and geophysical data will result in an improved final product. The calculated geophysical signal from two distinct geological interpretations shows how well a certain litho-structural model conforms with the geophysical data. Applications are in determine the position of rocks of distinct physical properties, checking the geometry of faults and extending mapped structures into inaccessible/covered areas.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".