S3 mineral targeting for porphyry copper exploration using natural field electromagnetics and magnetics: A case study from the Huckleberry and Berg–Ootsa Cu–Mo porphyry projects, near Houston, BC, Canada
Bibliographic record
Abstract
Abstract The Huckleberry project and porphyry copper–molybdenum mine and adjoining Berg and Ootsa porphyry projects are located approximately 90 km southwest of Houston, British Columbia (Figure 1). Together they form a large and rich mineral endowment, with as many as six known porphyry deposits and numerous occurrences, making them ideally suited for study using airborne geophysical methods. In 2021, Geotech Ltd. carried out a helicopter-borne natural field electromagnetic (NFEM) and magnetic survey over the Berg–Ootsa and Huckleberry projects that consisted of a combined 5251 line-km of coverage. Analyses of the airborne geophysical responses and 3D inversion results show that the known porphyry deposits all coincide with well-defined subcircular or tabular resistivity lows and similar coincident magnetic high signatures but with varying degrees of size and amplitude. These geophysical signatures are consistent with those previously found in NFEM and other airborne electromagnetic surveys over other calc-alkaline type porphyry deposits in the Western Cordillera. Other similar geophysical signatures are observed, but efficient targeting of potential porphyries over the vast survey area proves difficult due to the large number and their variable nature. A semiautomated mineral targeting approach was implemented that uses a relatively objective, machine-learning-assisted method, which combines structural complexities, self-organizing map classifications, and supervised deep neural network (SDNN) targeting. Using the Huckleberry deposit as a training area, the SDNN targeting approach was extended to a larger area covering the Berg–Ootsa–Huckleberry projects and has identified most of the known porphyry deposits and prospects, as well as new areas for follow-up.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".