Bibliographic record
Abstract
Ground penetrating radar (GPR) is unique amongst geophysical tools in terms of its imaging resolution and the diversity of its applications. Since its commercialisation four decades ago, GPR has also been distinguished because of the prevalence of some of its purveyors to oversell the method’s capabilities, relying largely on the end users’ lack of understanding of the underlying physics. Early adopters in the 1980s and 90s were dismayed to find that environments suitable for its purported ubiquitous deep penetration capabilities were rare and that it required resistivities well into the 1000s of Ohm m. Regardless of the advances made in electronics and antenna design in the intervening decades, the fundamental limitations have not changed.Misconceptions, “specsmanship” and hype have continued to abound in the GPR marketplace, particularly in recent years. Systems purporting to penetrate hundreds of metres using “megawatt” transmitters from the former Eastern Bloc have been promoted for mineral exploration, particularly in Australia and Africa. Other pseudo-radar concepts, such as the use of beam forming to achieve kilometres of penetration with centimetre accuracy, or THz laser scanners which can detect individual diamonds deep underground, have generally targeted junior exploration groups who lack in-house geophysical guidance.This work provides an overview of the fundamentals of non-dispersive EM wave propagation in the ground and an examination of the recent published performance claims of some GPR and pseudo-GPR systems within the context of accepted EM theory. The accepted methods for potentially increasing GPR performance, given the emerging technologies such as novel transmitter and receiver designs and new GPR antennas, are also discussed.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".