Rapid three-dimensional inversion of multi-transmitter electromagnetic data using the spectral Lanczos decomposition method
Bibliographic record
Abstract
In this paper, we develop a new method of three-dimensional (3D) inversion of multi-transmitter electromagnetic data. We apply the spectral Lanczos decomposition method (SLDM) in the framework of the localized quasi-linear inversion introduced by Zhdanov and Tartaras (2002 Geophys. J. Int . 148 506–19). The SLDM makes it possible to find the regularized solution of the ill-posed inverse problem for all values of the regularization parameter α at once. As an illustration, we apply this technique for interpretation of the helicopter-borne electromagnetic (HEM) data over inhomogeneous geoelectrical structures, typical for mining exploration. This technique helps to accelerate HEM data inversion and provides a stable and focused image of the geoelectrical target. The new method and the corresponding computer code have been tested on synthetic data. The case history includes interpretation of HEM data collected by INCO Exploration in the Voisey's Bay area of Canada.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".