Assessing the workflow for regional-scale 3D geologic modeling: An example from the Sullivan time horizon, Purcell Anticlinorium East Kootenay region, southeastern British Columbia
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Bibliographic record
Abstract
Abstract We have developed a regional-scale 3D geologic model, highlighting the Mesoproterozoic Sullivan time horizon (approximately 1470 Ma) throughout the Purcell Anticlinorium in the East Kootenay region. This 3D geospatial model of the region is constrained with an extensive surface and subsurface database of stratigraphic, structural, and geophysical observations distributed throughout the study area. This modeling exercise was conducted over a four-year period from 2011 to 2015 in which several iterations of the model were produced. The final model includes what is locally referred to as the Lower-Middle Aldridge stratigraphic contact (LMC), a map unit at the very top of the lower Aldridge Formation where the Sullivan world-class Pb-Zn-Ag deposit is located. Local mineral exploration initiatives focus on this key exploration target horizon, which is now modeled in 3D. The regional LMC model provides a much needed geospatial reference used to characterize and understand sedimentary exhalative, a type of ore deposit (SEDEX) ore systems as well as a key 3D exploration target. Developing regional 3D geologic models for orogenic interiors such as the Purcell region, and older shield regions is a challenge. This is largely due to data sparsity at depth, lack of standards for 3D data collection, storage, integration, and interpretation practice. Current algorithms use only a partial set of available observational or knowledge constraints and exist in workflows that do not allow for complicated geologic event histories. We have mitigated some of these challenges with a new implicit algorithm (SURFE), applied in this Purcell case study, to estimate major regional faults and horizons through variably distributed and clustered data. These modeled geologic elements are then fed into the SKUA structural and stratigraphic workflow to produce the volumetric model. Reflection on the general 3D modeling workflow for these regional situations highlights the need for embedding more knowledge constraints into the process.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it