Enhancing Ground-Penetrating Radar (GPR) Data Resolution Through Weakly Supervised Learning
Bibliographic record
Abstract
Ground-penetrating radar (GPR) is a pivotal noninvasive tool that yields subsurface images critical to archeology, near-surface characterization, geotechnical studies, and disaster response. The antenna central frequency of the GPR system has a significant impact on penetration depth and resolution. Lower antenna frequencies penetrate deeper but at lower resolutions, while higher frequencies offer detailed images at reduced depths. Therefore, improving the resolution of low-frequency radar with increased detection depth is an essential research focus. Inspired by image super-resolution advancements, supervised deep learning methods that rely on strictly paired training data have achieved remarkable success. However, acquiring such paired samples in practical scenarios is often a formidable challenge. To tackle this, we propose a novel resolution enhancement technique through weakly supervised learning, effectively addressing the scarcity of strictly paired samples in real-world situations. We utilize two sets of antennas with different central frequencies to construct our training data, with a low-frequency antenna as input and a high-frequency antenna as the learning target. A cycle-consistent generative adversarial network (Cycle-GAN) is trained to discern the mapping between low-resolution inputs and unpaired high-resolution data. The refined network is then employed to improve low-frequency GPR data resolution. Our work is validated on synthetic and real-world datasets. The proposed method effectively strengthens critical high-frequency details for finer imaging and broadens the frequency bandwidth. Significantly, it enhances resolution without compromising the detection depth of low-resolution GPR data, marking a substantial advancement in subsurface imaging technology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".