Exploration Drillhole Targeting with Gocad: Recent Advances in 3D Model Construction, Query and Visualisation
Bibliographic record
Abstract
The work described here summarises an early experiment in multidisciplinary, integrated 3D GIS for exploration drillhole targeting using the Gocad technology. The objective of the project was to prove the feasibility of 3D model construction and multi-layer spatial data query and analysis using a typically incomplete and inconsistent property-scale mineral exploration data set. The data are from a junior mining company exploring for gold on the south Carlin Trend, Nevada, U.S.A.A 3D "topological" structural model of fault blocks and horizons was constructed based on surface fault and contact mapping, and sections interpreted from sparse drilling. The resulting 3D representation corresponds to the vector geological map layer in 2D GIS. A 3D grid was superimposed on the model volume, corresponding to a raster grid in 2D GIS, in which each grid cell "knows" to which fault block and formation it belongs. Several more layers of geophysical, geochemical, and spatial-topological data were added to the 3D grid model, which was then used as a basis for query and analysis using Boolean and numerical operations. 3D spatial queries, like their counterpart in 2D GIS, correspond to conventional exploration reasoning for highlighting model sub-volumes favourable to mineral occurrence, and thus likely drillhole targets. They may also be used in developing an understanding of the multidisciplinary data relationships that define mineral occurrence.Results of this case study demonstrate that a trained user of the technology can construct an advanced 3D topological model of a property containing several formations, multiple fault blocks, drilling, geophysical and geochemical data within a time frame (approximately 10-15 days in this case) that is very short in comparison to project cycle times and data acquisition costs. The value of the model to the exploration team in this case study was regarded as very high, and is now being used as a framework for guiding ongoing exploration decisions.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".