Constrained Potential Field Inversions in Areas under Cover: Examples from Gawler Craton IOCG Prospects
Bibliographic record
Abstract
The future of greenfields mineral exploration will be driven towards covered terranes with little or no outcrop. Consequently, the inherent risk and costs of such exploration will rise. The exploration focus will be pushed towards inexpensive methods and more importantly obtaining the most value from them. Potential field geophysics provide a solution to this impending issue with regional datasets often available in the public domain and higher resolution data being relatively inexpensive to acquire. Constrained potential field inversion represents a method for adding or maximising the value from the associated datasets. Many greenfields environments have an apparent absence of a priori data to constrain the first pass inversion. This paper suggests that although this absence may exist, meaningful ?soft? constraints will still be present which when included in the model objective function, improve and add value to the inversion process. Additionally the same constraints can be used to test whether a proposed geological hypothesis is a viable model. Using gravity data over covered IOCG prospects within the Gawler Craton, this paper demonstrates how ?soft? constraints can be employed to enhance the inversion process. Simplified layered geological models representing cover and basement have been discretised, using realistic petrophysical bounds that when incorporated into the model objective function yield more accurate results. Furthermore, the potential of a prospect to host IOCG mineralisation can be simply tested in a similar fashion. When inversion results describe bodies that are geologically unrealistic, the target can be downgraded saving a potentially expensive drillhole.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".