A novel approach to 3D modelling ground-penetrating radar (GPR) data – A case study of a cemetery and applications for criminal investigation
Bibliographic record
Abstract
Ground-penetrating radar (GPR) is an established geophysical technique used extensively for the accurate reconstruction of the shallow (<10 m) subsurface. Reconstructions have largely been completed and presented as 2D vertical and horizontal planes, leaving limited visualization of subsurface 3D shapes and their spatial relationships. With technological advancements, particularly the availability and integration of various software platforms, 3D modelling of GPR data is now emerging as the new standard. However, despite these developments, there remains an inadequate examination and testing of these techniques, particularly in determining if their application is beneficial and warranted. In this study we conducted a GPR grid survey on a churchyard cemetery to generate and evaluate 2D and 3D-modelled reconstructions of the cemetery burial sites. Data collection and processing was completed using a Sensors and Software Incorporated pulseEKKO™ Pro SmartCart GPR system and EKKO_Project™ software, respectively. The modelling component was achieved using Schlumberger's Petrel™ E & P software platform, which is tailored to the petroleum industry. The subsurface patterns present in the 2D and 3D models closely matched the cemetery plot plan, validating our data collection, processing, and modelling methods. Both models were adequate for 2D horizontal visualization of reflection patterns at any specific depth. The 3D model was used to identify the presence of a companion burial plot (stacked caskets) and possible leachate plumes below and encircling burial sites, both of which were not evident in the 2D model, highlighting the benefits of 3D modelling when discerning subsurface objects. We expect our findings to be of value to similar GPR studies, with particular significance to geoforensic studies and criminal investigations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".