Pseudo-full-waveform inversion of borehole GPR data using stochastic tomography
Bibliographic record
Abstract
Abstract Electromagnetic full-waveform tomography is computer intensive and requires good knowledge of antenna characteristics and ground coupling. As a result, ground-penetrating-radar tomography usually uses only the first wavelet's arrival time and amplitude data. We propose to improve the classical approach by inverting multiple slowness and attenuation fields using stochastic tomography. To do so, we model the slowness and attenuation covariance functions to generate geostatistical simulations that are conditional to the arrival times, amplitudes, slowness, and attenuation observed along boreholes. We combine slowness and attenuation fields to compute conductivity and permittivity fields from which we model synthetic radar traces using a finite-difference time-domain full-waveform algorithm. Modeled traces that best match the measured ones correspond to the computed conductivity and permittivity fields that correlate best with the true physical properties of the ground. We apply the method to a synthetic example with known electric properties. We show that a combination of stochastic tomography and full-waveform modeling allows a better selection of permittivity fields close to the reference field, at a reasonable computing cost.
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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 | 0.002 |
| 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 | 0.001 |
| 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".