Three-dimensional stochastic assimilation of gravity data in Lalor volcanogenic massive sulphide, Manitoba, Canada
Bibliographic record
Abstract
We propose a new numerical workflow based on stochastic data integration where we merge a conceptual geological model, drillhole geophysical and geological logs, and surface geophysical data to compute a unified numerical model of a volcanogenic massive sulphide (VMS) deposit. The first step of the workflow consists in building a three-dimensional (3D) numerical conceptual model of the geology. This conceptual model, as well as geological logs, is then used to generate multiple equiprobable scenarios of the geology by means of multiple-point simulation (MPS). The MPS method studies high-order statistics in the space of a numerical conceptual model, making it possible to reproduce complex geological structures. We then use conventional conditional sequential Gaussian simulation, which is a method based on a node-by-node sequential process, to stochastically populate the geological grid with densities. For this purpose we use available density logs to simulate multiple equiprobable spatial distributions of the density at high spatial resolution within each geological unit separately. The stochastic high-resolution density models are iteratively combined by the gradual deformation method to minimize the difference between measured Bouguer anomaly data and the data computed on the combined realizations of density. Application of the proposed method to the Lalor deposit, a VMS deposit in Manitoba, Canada, produces a density model that honours the geology of the deposit and the Bouguer anomaly data. This unified model has the advantage to include all the available information (geological and density logs and surface geophysics) at scales appropriate for mining applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".