Coherent noise attenuation in GPR data by linear and parabolic Radon Transform techniques
Bibliographic record
Abstract
Ground Penetrating Radar (GPR) is a geophysical method increasingly used in numerous shallow applications. Unfortunately, electronic or acquisition problems can cause the presence in the radargrams of coherent noise interfering with the useful signal. A commonly observed phenomenon, especially for not-shielded antennae, is the surface-scattering effect, due to reflection or diffraction from above-surface objects. These noise events appear with a characteristic hyperbolic moveout in the usual common-offset sections. Other frequent problems are related to the presence of horizontal or dipping features due to system-ringing or other non-geological causes. Several methods have been tried to overcome these problems, most of which involve time domain or Fourier domain filtering. This work presents an attempt to reduce some of these noise modes by an original adaptation of filtering techniques implemented in the Radon domain. The Radon Transform (RT), both in the linear (or t-p) and in the parabolic version (or t-q), has been widely used in seismic processing, especially for multiple removal, but is still quite unfamiliar to GPR practitioners. The results achieved by different RT based methods for coherent noise attenuation in a GPR field example, compared to those of more conventional techniques, are quite encouraging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".