Removal of wavelet dispersion from ground-penetrating radar data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Wavelet dispersion caused by frequency-dependent attenuation is a common occurrence in ground-penetrating radar (GPR) data, and is displayed in the radar image as a characteristic “blurriness” that increases with depth. Correcting for wavelet dispersion is an important step that should be performed before GPR data are used for either qualitative interpretation or the quantitative determination of subsurface electrical properties. Over the bandwidth of a GPR wavelet, the attenuation of electromagnetic waves in many geological materials is approximately linear with frequency. As a result, the change in shape of a radar pulse as it propagates through these materials can be well described using one parameter, Q∗, 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 becomes one of determining Q∗ in the subsurface and deconvolving its effects using an inverse-Q filter. We present a method for the estimation of subsurface Q∗ from reflection GPR data based on a technique developed for seismic attenuation tomography. Essentially, Q∗ is computed from the downshift in the dominant frequency of the GPR signal with time. Once Q∗ has been obtained, we propose a damped-least-squares inverse-Q filtering scheme based on a causal, linear model for constant-Q wave propagation as a means of removing wavelet dispersion. Tests on synthetic and field data indicate that these steps can be very effective at enhancing the resolution of the GPR image.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it