Hydrogeological prospecting using P‐ and S‐wave landstreamer seismic reflection methods
Bibliographic record
Abstract
ABSTRACT We present two case histories from different areas and geological settings in Canada where we have used a vibrating seismic source coupled to a landstreamer receiver array in hydrogeological investigations related to aquifers in glacial sediments. In Manitoba, our P‐wave seismic reflection profiles are used to provide an assessment of the subsurface architecture of buried valleys, estimate the thickness and properties of both the channel fill and the overlying sediments to depths of ~100 m and locate optimum sites for groundwater well placements. In eastern Ontario, we collected P‐ and S‐wave seismic reflection as well as electrical resistivity data to investigate buried esker aquifers. The geophysical data provide detailed high‐resolution information (to ~30 m depth) on the structure of the esker core and its overlying sand cover and on the thickness and variability of the overlying fine‐grained aquitard. The data presented in this paper demonstrate that shallow seismic reflection methods are very effective tools to explore, assess and evaluate groundwater reservoirs and resources. The recent advent of landstreamer receiver arrays, especially when coupled to a vibratory seismic source, makes these methods significantly more cost‐effective and efficient. We now routinely collect ~1000 records/day, or 1.5‐6 line‐km/day, using our Minivib/landstreamer data acquisition system. With this type of efficient data collection, it is anticipated that the use of shallow seismic reflection methods in hydrogeological prospecting will increase as groundwater and its protection become more valued by society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".