Detection Of Higher Mode Surface Waves Over Unconsolidated Sediments By The Mx4 Wmethod
Bibliographic record
Abstract
In engineering application of surface waves it is critically important to accurately extract the fundamental mode dispersion curve. Among several factors that may adversely affect the extraction is the existence of higher modes with significant amount of energy. A calculated phase velocity can be an average of the fundamental and the higher-modes phase velocities or it can be the phase velocity of a specific higher mode, depending upon the specific method used for the application, unless the higher modes are properly handled during the data acquisition and processing steps. Therefore, it will have a practical value to observe the higher mode generation through field experiments and examine for any parameter that can be controlled during data acquisition. A higher mode (the first overtone) of high frequency (5-30 Hz) surface waves was observed by using the multi-channel analysis of surface waves (MASW) method at three boreholes located in unconsolidated sediments in the Fraser River Delta, near Vancouver, British Columbia. Each site has a unique near-surface shear (S)-wave velocity (Vs) structure as verified from downhole Ys measurements. The relative dominance of higher mode energy is examined in association with source distance as well as Ys structure. Our examination indicates that energy of higher modes tends to become more significant as the source distance becomes greater. It also reveals that the dominance may be related to a Vs structure: a greater dominance as fi changes little with depth, or fi has an overall low value, or a combination. The dependency on the source distance is observed to be stronger than that on the Vs structure. Attempts are made to explain the dependency by referring to one or a combination of three factors: attenuation, the near-field effects, and the intrinsic nature of surface waves. Inclusion of higher mode during a surface wave measurement for near-surface (~30 m) application can be either an advantage or a-disadvantage, depending on the specific type of application and the method used during the data acquisition and processing steps. It is, therefore, important to recognize through field observations those conditions both favorable and unfavorable to the generation of higher modes of high-frequency surface waves.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".