Experimental study of near‐field effects in multichannel array‐based surface wave velocity measurements
Bibliographic record
Abstract
ABSTRACT This paper examines the influence of source offset distance on surface wave phase velocity values determined from frequency‐wavenumber processing of multi‐channel data. Experimental surface wave data were collected over a broad range of frequencies at eleven deep soil sites in the Mississippi embayment of the central United States. Using analyses of multiple array configurations at each site, near‐field phase velocity values (determined with the source close to the array) were compared to far‐field velocity values. The source offset distance was expressed as a normalized value calculated as the distance from the source to the centre of the array, divided by the wavelength. The results from these field measurements showed that the influence of near‐field effects became evident when the normalized source offset distance was 0.5 or less, a value that is less restrictive than values determined from a recent study of near‐field effects using numerical simulations and experimental data. A possible reason for this discrepancy is the high Poisson’s ratio values in this study due to shallow water tables at the field test sites, a condition that was not examined in the previous study. Last, the effectiveness of processing using cylindrical beamforming for mitigating near‐field effects is also examined in this study and shown to provide a small improvement in velocity estimates in the near‐field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".