The Impact on Geological and Hydrogeological Mapping Results of Moving from Ground to Airborne TEM
Bibliographic record
Abstract
Abstract In the past three decades, airborne electromagnetic (AEM) systems have been used for many groundwater exploration purposes. This contribution of airborne geophysics for both groundwater resource mapping and water quality evaluations and management has increased dramatically over the past ten years, proving how these systems are appropriate for large-scale and efficient groundwater surveying. One of the major reasons for its popularity is the time and cost efficiency in producing spatially extensive datasets that can be applied to multiple purposes. In this paper, we carry out a simple, yet rigorous, simulation showing the impact of an AEM dataset towards hydrogeological mapping, comparing it to having only a ground-based transient electromagnetic (TEM) dataset (even if large and dense), and to having only boreholes. We start from an AEM survey and then simulate two different ground TEM datasets: a high resolution survey and a reconnaissance survey. The electrical resistivity model, which is the final geophysical product after data processing and inversion, changes with different levels of data density. We then extend the study to describe the impact on the geological and hydrogeological output models, which can be derived from these different geophysical results, and the potential consequences for groundwater management. Different data density results in significant differences not only in the spatial resolution of the output resistivity model, but also in the model uncertainty, the accuracy of geological interpretations and, in turn, the appropriateness of groundwater management decisions. The AEM dataset provides high resolution results and well-connected geological interpretations, which result in a more detailed and confident description of all of the existing geological structures. In contrast, a low density dataset from a ground-based TEM survey yields low resolution resistivity models, and an uncertain description of the geological setting.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".