Critical analysis of multiple-points statistics methods in the stochastic simulation of geology at Fox Kimberlitic Diamond Pipe located on the Ekati Property, North West Territories
Bibliographic record
Abstract
Multiple-point simulation (MPS) methods have been developed over the past decade as a mean to generate stochastic simulations while reproducing complex geological patterns, such as high-grade depositional veins, groups of high-grade lentil shaped orebodies, or the spatial geometries and patterns of diamond-bearing kimberlite pipes. This thesis compares two MPS methods by modelling the geology of a diamond pipe located at the Ekati mine, NWT, Canada. The single normal equation simulation algorithm SNESIM, which captures different patterns from a training image (TI), and the filter simulation algorithm FILTERSIM, which classifies the patterns founded on the TI, are considered in this study. Both methods are used to generate stochastic simulations of a four-category geology model containing crater, diatreme, xenoliths and host rocks. Soft information about the location of the host rock is also used. Both MPS methods reasonably reproduced the geometry of the pipe, as per the TI used (crater and diatreme rock units); however, the methods differed in the proportion and location of the xenolith bodies within the pipe. The validation of the simulated results provided by the above methods shows a reasonable reproduction of the data proportions for all geological units considered; the validation of spatial statistics, however, shows that although simulated realizations from both methods reasonably reproduce the fourth order spatial statistics of the TI, they do not reproduce well the same spatial statistics of the available data (when these differ from the TI). An interesting observation is that SNESIM better imitates the shape of the pipe, whereas FILTERSIM reaches a better reproduction of the xenolith bodies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".