Lineament analysis as a tool for hydrocarbon and mineral exploration: a Canadian case study
Bibliographic record
Abstract
SummaryAn understanding of the geological framework and localized structural constraints are critical to hydrocarbon and mineral deposit exploration. Lineament tectonics has been used successfully to delineate global oil and ore deposits. Automated lineament routines are important to promote efficiency and consistency. We suggest an alternative approach to geophysical numerical methods. “Stream flow analysis” is commonly applied to topographic data to delineate stream locations, flow impact, and flow direction by identifying localized low points and their continuity on a topographic surface. In this study, we apply stream flow analysis to a “topographic” surface defined by aeromagnetic data, where faults and fractures are revealed since they are represented by magnetic lows. Conversely, magnetically high features, such as dykes, are delineated by changing the data set background value causing highs to be represented by lows. Furthermore, by constraining the dimensions of the “watershed” we are able to isolate linear features at multiple scales. Further analysis of stream segments involves direction /length studies, linearity analysis, and stream intersection points. Typically, geologic terranes will have a dominant fabric or fracture orientation due to the local tectonic history. Therefore if the linear directions are isolated along specific orientations, different geologic terranes are resolved. Ore deposits often occur along fracture systems since they act as a conduit for hydrothermal fluids. When multiple fractures culminate at a common intersection, the probability of mineralization increases. Thus, visualization of “stream intersection” points in conjunction with geophysical products will highlight exploration areas. These methodologies are applied to a study area in the Northwest Territories, Canada which has been shown to have high mineral potential and similar IOCG-type deposits as Olympic Dam in Australia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".