A new survey design for 3D IP inversion modelling at Copper Hill
Bibliographic record
Abstract
The Copper Hill prospect is a well known porphyry copper/gold system. Investigation of geochemical assay data and gradient array IP data suggest that grid northwest and grid northeast structural directions have considerable control over mineralisation. Previous drilling has been oblique to these directions.The sulphide rich mineralisation zones have moderate to strong IP responses. The intention of Golden Cross Resources Ltd, as tenement holders, was to verify the main trends and locations of possible extensions to mineralisation. A 3D inversion of IP data is a suitable approach to the problem of mapping the sulphide horizons in detail.The primary aim of the survey was to gather sufficient IP/resistivity data over an area 1.2km by 1km to generate a reasonably detailed 3D-inversion model for Copper Hill.Based on the knowledge that the data was to be processed in a 3D-inversion program, a modification of the pole-dipole IP survey geometry was used. The new array design resulted in fast collection of a large quantity of data. The high data redundancy inherent in the design allowed for editing prior to inversion.3D inversion results highlighted the three dimensionality of sulphides zones at Copper Hill. Zones of high IP response are concentrated along northwest and north trending structures forming an annular zone terminating at depth.A cost effective broad scale and detailed IP survey was successfully accomplished.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".