Multicomponent georadar data: Some important implications for data acquisition and processing
Bibliographic record
Abstract
Abstract Many seismic reflection processing techniques are applied routinely to ground-penetrating radar (georadar or GPR) data. Although similarities exist between seismic (acoustic) and radar wave propagation there are some significant differences, some of the most important of which are associated with the dipole nature (1) of georadar sources and receivers and (2) of elemental sources used to represent scattering bodies. Neglecting the dipole character of electromagnetic surveys may result in incomplete or biased images of the subsurface. In an attempt to understand better the consequences of recording dipolar wavefields, we have simulated numerous multicomponent georadar data sets. These simulations are based on the weak scattering (Born) approximation, such that point heterogeneities in the subsurface can be represented by infinitesimal dipoles with moments parallel and proportional to the incident georadar wavefields. The effects of depolarization and dispersion are not included. Nevertheless, many subsurface structures can be modeled by suites of appropriately distributed infinitesimal dipoles. Georadar images of even the simplest subsurface structures are shown to depend strongly on the relative orientations and positions of the source and receiver antennas. A positive aspect of dipolar wavefields is that multicomponent georadar profiles contain information on the locations of both in-plane and out-of-plane structures. Furthermore, “pseudoscalar” wavefields can be simulated from coincident georadar data sets acquired with two pairs of parallel source-receiver antennas, one oriented perpendicular to the other. Pseudoscalar georadar data, which are characterized by low degrees of directionality, can be processed (including migration) confidently using standard seismic processing software (assuming that dispersion is not a major problem). To illustrate the advantages of multicomponent georadar data, two field examples are presented. One demonstrates the value of recording dual-component georadar data along isolated profiles; the other shows the benefits of combining 3-D georadar data sets acquired with dual component source-receiver antenna pairs to form pseudoscalar wavefield images.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".