Application of P‐Wave Seismic Reflection Methods Using the Landstreamer/Minivib System to Near‐Surface Investigations
Bibliographic record
Abstract
Landstreamer receiver arrays greatly increase the efficiency of shallow seismic reflection surveys, and their use is becoming more widespread, particularly for the acquisition of SH-wave reflection or MASW data. This paper presents recent results using a P-wave landstreamer coupled to an IVI minivib source to acquire shallow seismic reflection profiles for near-surface investigations. The landstreamer/minivib system has proved to be an extremely efficient method of producing high-quality P-wave reflection data in a variety of geological settings. Comparison with traditional planted geophone data using an in-hole shotgun source show that, under optimum conditions for the in-hole shotgun source, the landstreamer data shows a decrease in the frequency of the reflection signal, but there may be an increase in the reflection frequencies where surface materials are dry and sandy. In either case, the advantages of greatly increased data production and the improved multi-channel capability (number of receivers and short offsets) are very significant. The technique has been successfully used to map buried valleys, and to define glacial stratigraphy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".