Inversion of airborne geophysics over the DO-27/DO-18 kimberlites — Part 3: Induced polarization
Bibliographic record
Abstract
The geologically distinct DO-27 and DO-18 kimberlites, often called the Tli Kwi Cho (TKC) kimberlites, have been used as a testbed for airborne geophysical methods applied to kimberlite exploration. This paper focuses on extracting chargeability information from time-domain electromagnetic (TEM) data. Three different TEM surveys, having similar coincident-loop geometry, have been carried out over TKC. Each records negative transients over the main kimberlite units and this is a signature of induced polarization (IP) effects. By applying a TEM-IP inversion workflow to a versatile time domain EM (VTEM) data set we decouple the EM and IP responses in the observations and then recover 3D pseudo-chargeability models at multiple times. A subsequent analysis is used to recover Cole-Cole parameters. Our models demonstrate that both DO-18 and DO-27 pipes are chargeable, but they have different Cole-Cole time constants: 110 and 1160 μs, respectively. At DO-27, we also distinguish between two adjacent kimberlite units based on their respective Cole-Cole time constants. Our chargeability models are combined with the density, magnetic susceptibility and conductivity models to build a 3D petrophysical model of TKC using only information obtained from airborne geophysics. Comparison of this final petrophysical model to a 3D geological model derived from the extensive drilling program demonstrates that we can characterize the three main kimberlite units at TKC: HK, VK, and PK in three dimensions by using airborne geophysics.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".