In situ shear wave velocity from multichannel analysis of surface waves (MASW) tests at eight Norwegian research sites
Bibliographic record
Abstract
The multichannel analysis of surface waves (MASW) technique, which is used to determine shear wave velocity (Vs) and hence small strain stiffness (Gmax), has recently generated considerable interest in the geophysics community. This is because of the ease of carrying out the test and analysis of the data. The objective of this work was to assess the repeatability, accuracy, and reliability of MASW surface wave measurements for use in engineering studies. Tests were carried out at eight well-characterized Norwegian clay, silt, and sand research sites where Vs had already been assessed using independent means. As well as being easy and quick to use, the MASW technique gave consistent and repeatable results, and the MASW Vs profiles for the clay sites were similar to those obtained from other techniques. Reasonable results were also obtained for the silt and sand sites, with the best result being obtained for the finer silt. This work also confirms that MASW Vs clay profiles are comparable to those obtained by correlation with cone penetration test (CPT) data. For these sites there also seems to be a good correlation between normalized small strain shear modulus and in situ void ratio or water content, and the data fit well with published correlations for clays.Key words: soft clays, silts, sands, small strain stiffness, shear wave velocity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".