Geological modeling using a recursive convolutional neural networks approach
Bibliographic record
Abstract
Resource models are constrained by the extent of geological units that often depend on the lithology, alteration and mineralization. A three dimensional model of these geological units must be built from scarce information coming from drillholes and limited understanding about the geological setting in which the ore deposit is places. In this work, we present a new technique for multiple-point geostatistical simulation based on a recursive convolutional neural network approach (RCNN). The method requires conditioning data and a training image that depicts the type of geological structures expected to be found in the deposit. This training image is used to learn the patterns of categories found and these are imposed in the final simulated model conditioned by the categories found during logging at the actual drill-hole samples. A lithological modeling process is carried out in a copper deposit in Chile to demonstrate the method. Comparison with current techniques and spatial metrics are used to clarify concepts and RCNN properties. Also, strengths and weaknesses of the methodology are discussed by briefly reviewing the theoretical perspective and looking into some of its practical aspects.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".