<title>Estimation and correction of wavelet dispersion in GPR data</title>
Bibliographic record
Abstract
Wavelet dispersion caused by frequency-dependent attenuation is a common and significant problem in ground-penetrating radar (GPR) data. In the radar image, it is displayed as a characteristic 'blurriness' that increases with depth. Correcting for wavelet dispersion in GPR data is an important step that should be performed before either qualitative interpretation or quantitative determination of subsurface electrical properties are attempted. Over the bandwidth of a GPR wavelet, the attenuation of electromagnetic waves in many geologic materials is approximately linear with frequency. For this reason, the change in shape of a radar pulse as it propagates through these materials can be described using one parameter, Q*, which is related to the slope of the linear region. Assuming that all subsurface materials can be characterized by some Q* value, the problem of estimating and correcting for wavelet dispersion in GPR data becomes one of determining Q* in the subsurface and deconvolving its effects through the use of an inverse Q filter. A method for the estimation of Q* from GPR data based on a technique developed for seismic attenuation tomography is presented. Essentially, Q* is determined from the downshift in the dominant frequency of the GPR wavelet with time down a trace. Once Q* has been obtained, we propose an inverse Q filtering technique based on a causal, linear model for constant Q as a means of removing wavelet dispersion. Initial tests on field data indicate that this technique is very effective at enhancing the resolution of the GPR image.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.036 |
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".