3D Bayesian Inversion of Potential Fields: The Quebec Oka Carbonatite Complex Case Study
Bibliographic record
Abstract
Potential fields methods, based on the exploitation of gravity and magnetic fields, are among the most important methods to recover fundamental information on the Earth crust structure at global, regional and local scales. The bottleneck for this kind of geophysical methods is often represented by the development of ad-hoc techniques to fully exploit the available data. In fact, each different technique can observe the effect of a single property of the subsurface and when we want to estimate this property from the observed field (the so-called inverse problem), several problems such as non-uniqueness and instability arise. A possible solution to these problems consists in jointly inverting, in a consistent way, different observed fields, possibly also incorporating all the available geological constraints. In the current work, we present an innovative Bayesian algorithm aimed at performing a full 3D joint inversion of gravity and magnetic fields constrained by geological a-priori qualitative information. The algorithm is tested on a real-case scenario, namely, a local study to estimate a complete 3D model of the Oka carbonatite complex. This complex is a composite pluton in Quebec (Canada), important for mining operations related to critical raw material such as Niobium and other rare earth. This example shows the reliability of the developed inversion algorithm and gives hints on the fundamental role that potential fields can play in mining activities.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 teacher head, 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".