Examples of Improved Inversion of Different Airborne Electromagnetic Datasets Via Sharp Regularization
Bibliographic record
Abstract
Abstract Large geophysical datasets are produced routinely during airborne surveys. The Spatially Constrained Inversion (SCI) is capable of inverting these datasets in an efficient and effective way by using a 1D forward modeling and, at the same time, enforcing smoothness constraints between the model parameters. The smoothness constraints act both vertically within each 1D model discretizing the investigated volume and laterally between the adjacent soundings. Even if the traditional, smooth SCI has been proven to be very successful in reconstructing complex structures, sometimes it generates results where the formation boundaries are blurred and poorly match the real, abrupt changes in the underlying geology. Recently, to overcome this problem, the original (smooth) SCI algorithm has been extended to include sharp boundary reconstruction capabilities based on the Minimum Support regularization. By means of minimization of the volume where, the spatial model variation is non-vanishing (i.e., the support of the variation), sharp-SCI promotes the reconstruction of blocky solutions. In this paper, we apply the novel sharp-SCI method to different types of airborne electromagnetic datasets and, by comparing the models against other geophysical and geological evidences, demonstrate the improved capabilities of in reconstructing sharp features.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".