Inverting surface GPR data using FDTD simulation and automatic detection of reflections to estimate subsurface water content and geometry
Bibliographic record
Abstract
ABSTRACT A new inversion scheme for common-offset ground-penetrating radar measurements at multiple antenna separations was proposed, which is intermediate between inverting of picked reflectors using ray-tracing and full-waveform inversion. The measurements are modeled similarly to the real data using 2D finite-difference time-domain simulations. These simulations are obtained with a parameterized model of the subsurface that consists of several layers with constant dielectric permittivity and an explicit representation of the layers’ interfaces. Then, reflections in the modeled and in the real data are detected automatically, and the reflections of interest of the real data are selected manually. The sum of squared residuals of the reflections’ traveltime and amplitude is iteratively minimized to estimate subsurface water content and geometry, i.e., the position and shape of the layer interfaces. The method was first tested with a synthetic data set and then applied to a real data set. The comparison of the method’s result with ground-truth data showed an agreement with the subsurface geometry within ±5 cm and with the water content, a difference less than ±2% volume.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".