Geostatistical Analysis of Vein Geometry: A Comparative Study and Proposed Framework for Improved Resource Estimation
Bibliographic record
Abstract
Vein-type gold deposits are pivotal within the mining industry, yet accurately defining the boundaries between mineralized veins and host rocks remains a persistent challenge. These delineations are crucial for precise resource estimation and directly impact the economic viability of mining projects. Existing methodologies, including conventional geostatistical techniques, often fail to adequately capture the complex geological nature of vein deposits, potentially leading to the under- or over-estimation of ore reserves. This study seeks to address these limitations by enhancing boundary definition methods through the application of advanced geostatistical techniques such as indicator kriging and cokriging. A comprehensive case study, utilizing over 14,000 drilling samples, was conducted to assess the accuracy and practical applicability of these methods in estimating vein boundaries. Each method presents distinct advantages and disadvantages: Indicator Kriging quantifies the probability of belonging to the vein, providing straightforward categorization, while cokriging utilizes spatial correlations between variables for improved accuracy. Although indicator kriging is simpler to implement, co-kriging is provided more precise boundary delineation due to its ability to incorporate multiple variables. By the conclusion of this study, it was determined that Cokriging, by leveraging spatial correlations between variables, proved more effective than Indicator Kriging for vein-type deposits. Given the unique characteristics of each vein-type gold deposit, automating the procedure for determining optimum parameters is essential. This automation not only improves the modeling process by providing the most suitable approach for each specific case but also ensures the generation of more accurate and reliable data, which significantly affects long-term mining decisions and economic outcomes. An automated framework, developed using Python and GSLIB, facilitated the efficient execution of these techniques. The findings are expected to significantly mitigate boundary estimation errors, ultimately leading to more reliable resource estimates and more informed decision-making, thus enhancing the overall economic feasibility of mining projects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".