Inversion of Multi‐source TEM data with Applications to Mt. Milligan
Bibliographic record
Abstract
We present a practical formulation for forward modeling and inverting time domain data arising from multiple transmitters. The underpinning of our procedure is the ability to factor the forward modeling matrix and then solve our system using direct methods. We formulate Maxwell's equations in terms of the magnetic field, H and discretize the equations using a finite volume technique in space and a backward Euler in time. The MUMPS software package is used to carry out a decomposition of the forward operator, with the work distributed over an array of processors. The forward modeling is then quickly carried out using the factored operator. The factorization allows traditional Gauss-Newton inversion mthodologies to be implemented with greater efficiency than could be obtained from iterative techniques. As a demonstration we invert VTEM data at Mt. Milligan which is a Cu-Au porphyry deposit in British Columbia. 1D inversions produce a conductive artifact at depth that is inconsistent with geology. 3D inversions however, even from a limited number of stations, yield a more realistic result. Through the use of a synthetic model that emulates the geology at Mt. Milligan, we are able to show why the geologic artifacts arise from the 1D inversions. Lastly for the field data, we show how the resolution of the inversion result is further enhanced as progressively more transmitters are added.
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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 | 0.001 | 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".