Fast approximate 2D inversion of airborne TEM data: Born approximation and empirical approach
Bibliographic record
Abstract
ABSTRACT Airborne transient electromagnetic surveying provides data sections with a sufficient coverage to perform 2D imaging of electrical conductivity within the ground. Full 2D inversion using numerical modeling with finite differences or finite elements is still a time-consuming method to process the large amount of data acquired during an airborne survey. Two-dimensional structures increase the complexity of eddy current patterns within the ground. Consequently, fast approximate imaging using 2D sensitivities of equivalent homogeneous media is not sufficient, and causes strong artifacts in the resulting model. To overcome this problem, one prefers to use 1D inversion or 3D inversion using local sensitivity to process this kind of data. However, we consider a fast 2D inversion to be reachable. By estimating numerically the 2D sensitivity caused by 2D perturbations and showing that it differs considerably from the ones derived from homogeneous media, we propose an empirical model for in-loop configuration which describes the numerical 2D sensitivity. We applied this method to synthetic data; it eliminated 2D artifacts which are often encountered when using approximate inverse methods based on the theory of equivalent homogeneous half-space. An application to real in-loop data illustrates this improvement for imaging a dipping layer of conductive graphite deposits in Canada. This method is relatively fast. It could provide a better understanding of the ground during the survey, and would allow geophysicists to better manage the whole campaign.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".