Spectral velocity analysis for the determination of ground‐wave velocities and their uncertainties in multi‐offset GPR data
Bibliographic record
Abstract
ABSTRACT In many hydrological applications, ground‐wave velocity measurements are increasingly used to map and monitor shallow soil water content. In this study, we propose an automated spectral velocity analysis method to determine the direct ground‐wave (DGW) velocity from common midpoint (CMP) or multi‐offset ground‐penetrating radar (GPR) data. The method introduced in this paper is a variation of the well‐known spectral velocity analysis for seismic and GPR reflection events where velocity spectra are computed using different coherency measures along hyperbolas following the normal moveout model. Here, the unnormalized cross‐correlation is computed between waveforms across data gathers that are corrected with a linear moveout equation using a predefined range of velocities. Peaks in the resulting velocity spectra identify linear events in the GPR data gathers like DGW events and allow for estimating the corresponding velocities. In addition to obtaining a DGW velocity measurement, we propose a robust method to estimate the associated velocity uncertainties based on the width of the peak in the calculated velocity spectrum. Our proposed method is tested on synthetic data examples to evaluate the influence of subsurface velocity, surveying geometry and signal frequency on the accuracy of estimated ground‐wave velocities. In addition, we investigate the influence of such velocity uncertainties on subsequent soil water content estimates using an established petrophysical relationship. Furthermore, we apply our approach to analyse field data, which were collected across a test site in Canada to monitor a wide range of seasonal soil moisture variations. A comparison between our spectral velocity estimates and results derived from manually picked ground‐wave arrivals shows good agreement, which illustrates that our spectral velocity analysis is a feasible tool to analyse DGW arrivals in multi‐offset GPR data gathers in an objective and more automated manner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".