Constraining gravity and magnetics inversions for mineral exploration using limited geological data
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".