Building probabilistic quasi-geology models and mapping mineral resources using joint inversion and geology differentiation
Bibliographic record
Abstract
<!--!introduction!--> Multiple data sets are typically collected in an airborne survey. The standard way of interpreting multiple airborne data sets is to invert them separately to obtain a set of physical property models, e.g., a density contrast and a susceptibility model. There are two issues with this approach. First, it does not make use of the complementary information contained in different data sets. Secondly, it is not straightforward to interpret multiple physical property models in terms of geological structures and compositions. We propose a new workflow to integrate the information from multiple geophysical data sets and prior geology information, if available, into a 3D quasi-geology model. This new workflow has two components: mixed Lp norm joint inversion and geology differentiation. Joint inversion allows for the reconstruction of structurally consistent physical property models. Geology differentiation is a process of classifying the recovered physical property values into distinct classes, each of which is characterized by a unique range of physical property values and can be interpreted as an individual geological unit. We have applied this workflow to a set of airborne geophysical data over the Decorah area in northeast Iowa, USA, and successfully created a probabilistic quasi-geology model that informs the geological structures and compositions in this area. The workflow has also been applied to the multiple airborne data sets collected over the QUEST project area in British Columbia, Canada, to help map prospective areas of mineral resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".