3D modeling of buried valley geology using airborne electromagnetic data
Bibliographic record
Abstract
Abstract Buried valleys are important hydrogeologic features of glaciated terrains. They often contain valuable groundwater resources; however, they can remain undetected by borehole-based hydrogeologic mapping or prospecting campaigns. Airborne electromagnetic (AEM) surveys provide high-density information that can allow detailed features of buried valleys to be efficiently mapped over large geographic areas. Using AEM data for the Spiritwood Valley Aquifer system in Manitoba, Canada, we developed a 3D electric property model and a geologic model of the buried valley network. The 3D models were derived from voxel-based segmentation of electric resistivity obtained via spatially constrained inversion of two separate helicopter time-domain electromagnetic data sets (AeroTEM and versatile time-domain electromagnetic [VTEM]) collected over the survey area. Because the electric resistivity do not provide unequivocal information on subsurface lithology, we have used a cognitive procedure to interpret the electric property models of the aquifer complex, while simultaneously incorporating supporting information for the assignment of lithology in the 3D geologic model. For the Spiritwood model, supporting information included seismic reflection data and borehole records. These data constrained valley geometry and provided lithologic benchmarks at specific borehole sites and along seismic transects. The large-scale AeroTEM survey provided the basis for modeling the regional extent and connectivity of the Spiritwood Valley Aquifer system, whereas the local-scale VTEM survey provided higher near-surface resolution and insight into a detailed shallow architecture of individual buried valleys and their fill.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".