Three-dimensional inversion of airborne time-domain electromagnetic data with applications to a porphyry deposit
Bibliographic record
Abstract
ABSTRACT We inverted airborne time-domain electromagnetic (ATEM) data over a porphyry deposit in central British Columbia, Canada and recovered the 3D electrical conductivity structure. Full 3D inversion was required because of the circular geometry of the deposit. Typical analysis, which assumes a homogeneous or layered earth, produces conductive artifacts that are contrary to geologic expectations. A synthetic example showed that those misleading artifacts arise by assuming a 1D layered earth and that a 3D inversion can successfully solve the problem. Because of the computational challenges of solving the 3D inversion with many transmitters of airborne survey, we introduced a work flow that uses a multimesh strategy to handle the field data. In our inversion, a coarse mesh and a small number of soundings are first used to rapidly reconstruct a large-scale distribution of conductivity. The mesh is then refined and more soundings are incorporated to better resolve small-scale features. This strategy significantly speeds up the 3D inversion. The progressive refinement of the mesh also helps find the resolution limit of the data and an appropriate mesh for inversion, thus overcomputing on an unnecessarily fine mesh can be avoided. The final conductivity structure has features that emulate the expected geologic structure for a porphyry system and this substantiates the need and capability for working in 3D. However, the necessity for using 3D can depend upon the EM system used. A previous 1D interpretation of frequency-domain EM data at Mt. Milligan indicated a resistive stock. We reconciled this result with the present by computing the footprints of the frequency and time-domain surveys. The distribution of currents for the frequency-domain system was smaller than the length scale of the geologic target while the opposite was true for the time-domain data.
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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 | 0.000 | 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".