Combination of HVSR and MASW Methods to Obtain Shear Wave Velocity Model of Subsurface in Israel
Bibliographic record
Abstract
Estimating possible site effect is an integral part of evaluation of the seismic hazard and reduction of earthquake damages. In regions with low or moderate seismicity as in Israel, the site response should be determined by analytical tools. These computations require knowledge of the subsurface geological structure in terms of shear-wave velocity (Vs) profile down to seismic bedrock. Conventionally, this problem is resolved by joint implementation of Horizontal-to-Vertical Spectral Ratios (HVSR or Nakamura’s) technique, which is based on ambient noise measurements and seismic methods such as S-wave refraction or Multichannel Analysis of Surface Waves (MASW) method. The first one does not allow deep penetration of seismic waves because of its weak source. The MASW method using 4.5 Hz geophones is restricted in penetration depth of surface waves because of frequency (wavelength) limitations. In this study, we have applied 2.5 Hz geophones and special data processing to provide constructing Vs section to a depth of 100 m and deeper. In combination with HVSR measurements, MASW enables constructing reliable subsurface model, which could be integrated into the seismic hazard assessment. Testing of this combined methodology was carried out at a number of sites with differing geological structures in Israel.
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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 | 0.000 |
| 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".