Crosshole GPR traveltime tomography in elliptically anisotropic media
Bibliographic record
Abstract
Owing to relatively rapid water content variations with respect to electromagnetic wavelength at GPR frequencies, the vadose zone usually exhibits significant velocity anisotropy. Neglecting anisotropy in traveltime tomographic reconstruction leads to artifacts that can obscure important subsurface features, the vadose zone being subject to this problem. In this paper, an algorithm for crosshole GPR geostatistical traveltime tomography in elliptically anisotropic media is presented. The advantages of the geostatistical tomography algorithm are that the solution is regularized by the covariance of the model parameters and that stochastic simulations can be performed to appraise the variability of the solution space. The implementation relies on a fast curved raytracing scheme specifically crafted for the problem at hand. The benefits of the algorithm to image the vadose zone are illustrated through a synthetic case that is representative of typical studies in quaternary geological settings. The results show that considering anisotropy yields better fit to the data at high ray angles and reduces reconstruction artifacts.
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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 | 0.000 |
| 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 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".