A rational Krylov subspace method for 3D modeling of grounded electrical source airborne time-domain electromagnetic data
Bibliographic record
Abstract
The rational Krylov subspace method enables the time integration required to calculate responses directly in the time-domain to be computed accurately and more efficiently than by regular time-stepping methods. In this study, the optimal rational Krylov subspace approach is used for the forward modeling of data from the grounded electric source airborne time-domain electromagnetic (GREATEM) method. The space dependence of Maxwell's equations is discretized using a mimetic finite-volume (MFV) technique, which allows strongly discontinuous conductivities to be treated properly. One advantage of an MFV approach is that the initial magnetic problem for the grounded electric source can be solved using the same discrete operators. The optimal rational Krylov subspace approach is then used for the time integration to efficiently model the full spectrum with fewer solutions of a large system of equations. A concise optimization algorithm is presented to select a single repeated pole parameter, which results in convergence under an a priori given error independent of mesh grid and electrical structure. The direct solver ‘PARDISO’ and right preconditioning are used to further accelerate solution performance of solving the large asymmetrical linear system of equations. The accuracy and efficiency advantages are demonstrated by a large conductivity contrasts layered model and in a 3D benchmark model. A deeply buried massive sulfide model was also built up to evaluate the deep detection capability of the GREATEM method, which shows one can expect to detect a significant response from the deep target in the airborne measurements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".