3D modeling of grounded electric-source airborne time-domain electromagnetic data using rational Krylov subspace method
Bibliographic record
Abstract
The grounded electric source airborne time-domain electromagnetic (GREATEM) method has recently undergone a number of advances, including development of tools that can produce large source moments and hence allow for large transmitter–receiver offsets and thus greater depths of investigation. In this work, a combined mimetic finite volume and rational Krylov subspace (MFVRK) method is presented for modelling GREATEM data. The rational Krylov subspace scheme provides a more efficient method for the discretization of Maxwell’s equations in the time domain than directly using an implicit time-stepping strategy since many fewer large systems of equations need be solved. This MFVRK solver was tested using a model of a complex 3D conductor at a vertical contact. The results of this MFVRK approach agree well with those obtained by the MFVTD (mimetic finite volume and implicit time-stepping) and FDTD (time-domain finite difference) methods. A deep buried massive sulfide model was also used to evaluate the deep detection capability of the GREATEM method. The results show that by using the GREATEM approach we can expect to detect significant response from the deep target in the airborne measurements. Presentation Date: Wednesday, October 17, 2018 Start Time: 1:50:00 PM Location: 213A (Anaheim Convention Center) Presentation Type: Oral
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".