Constraining gravity and magnetics inversions for mineral exploration using limited geological data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mineral exploration produces a large amount of diverse geological and geophysical data, yet it can be difficult to combine all of this information into integrated models of subsurface geology. Gravity and magnetic data are the two most common geophysical datasets used in mineral exploration. They are commonly interpreted by developing 2D or 3D geological models, forward modelling the geophysical response, and modifying the models until they explain the observed data. Inversion techniques have also been developed to calculate 2D or 3D physical property models that explain observed geophysical responses. However, inversion of potential field data is hindered by the non-uniqueness of solutions. Application of default, geologically-unconstrained inversions to obtain estimated subsurface physical property models from gravity and aeromagnetic datasets is a common step in many exploration programs. Although the recovered models can help target anomalous features in the subsurface, a reliable model, consistent with all observed geological and geophysical information, can only be recovered by including geology-based constraints with the standard mathematical constraints. The University of British Columbia - Geophysical Inversion Facility?s (UBC-GIF) GRAV3D and MAG3D gravity and magnetic inversion packages (Li and Oldenburg, 1996, 1998) are particularly well suited to early stages of exploration where prior geological knowledge is limited.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it