Mapping hydrothermal footprints: case studies from the Meliadine Gold District, Nunavut
Bibliographic record
Abstract
The geochemical and mineralogic signature, or hydrothermal footprint, of ore deposits has great potential as an exploration tool. In some cases, hydrothermal footprints can be mapped up to several kilometers beyond the economically viable portion of the deposit and thus offers the potential to vector from sub-economic mineralization towards higher-grade ore from district- to deposit-scales. Conventionally, hydrothermal footprints are mapped using some preferred threshold concentration for each pathfinder element, or ratio, of interest. The results are then generally portrayed as stacked geochemical traverses adjacent to known ore bodies. However, the conventional approach inadequately accounts for the multivariate nature of ore processes and the inherently imprecise boundary between barren and mineralized rock. In this contribution we explore alternative methods to define and map the hydrothermal footprint at six gold deposits and ore zones within the Meliadine Gold District (MGD), Nunavut. MGD host rocks are variably altered (silicified ± sericitized ± sulphidized ± carbonatized ± chloritized) adjacent to BIF-hosted replacement-style gold mineralization and auriferous greenstone-hosted quartz (± ankerite) veins cutting mafic volcanic, interflow sediments and turbiditic successions. Robust principle component analysis defines key element assemblages (Au-Ag-As-S-Te-Bi-W-Sb) that are associated with gold and are enriched from 10s to 100s of meters adjacent to ore zones. We integrate and map pathfinder element enrichment and quantified measures of hydrothermal alteration intensity using a hybrid fuzzy- and conditional probability-based model (weights of evidence) in an effort to further highlight the complementary nature of multivariate datasets and to define fuzzy footprints. The available whole-rock data suggests that multi-element anomalies are, in some instances, better suited for defining broader geochemical anomalies than was apparent from analysis of individual pathfinder elements. We emphasize that samples containing pathfinder element concentrations in excess of some preferred threshold are akin to conventional definitions of geochemical anomalies, but in this case occur primarily in the ore zone and are thus of limited use for vectoring. In contrast, fuzzy footprints delineate the simultaneous occurrence of favourable pathfinder element enrichment and hydrothermal alteration for samples that would have been excluded following the conventional approach. These samples occur in hanging wall and footwall rocks devoid of gold (< 5 ppb), and thus provide a possible vector to high-grade gold ore.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".