The effects of receiver arrangement on velocity analysis with multi‐concurrent receiver GPR data
Bibliographic record
Abstract
Abstract Determining subsurface electromagnetic (EM) wave velocity is critical for ground‐penetrating radar (GPR) data analysis, as velocity is used for the time‐to‐depth conversion, and hence leads to obtaining the precise location of the objects of interest. Currently, the way to acquire detailed subsurface EM wave velocity models involves employing multi‐offset GPR surveys, such as wide‐angle reflection‐refraction (WARR), in conjunction with normal moveout (NMO) based velocity analysis. Traditionally, these surveys are carried out using two separate transducers and were, therefore, time‐consuming and had limited uptake. Recent advances in GPR hardware have allowed the development of novel systems with multi‐concurrent sampling receivers, which enable rapid and dense acquisition of WARR data. These additional receivers increase the overall size, weight and cost of the system. Therefore, we investigated the effects of receiver arrangement on NMO‐based velocity analysis and considered reducing the overall number of transducers, whilst maintaining satisfactory velocity spectra resolution and, hence, obtaining detailed stacking velocity models as well as improved stacked reflection sections. We used both simulated data from complex three‐dimensional models as well as field data and examined different numbers and positions of receivers in different environments. Our results show that velocity spectra resolution can be maintained within acceptable limits whilst reducing the number of receivers from a configuration with seven equally spaced receivers, to a sparse configuration of four receivers. Thus, being able to decrease the number of receivers used by these new GPR systems will reduce both the total system weight and cost and, hopefully, increase their adoption for GPR surveys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".