Probabilistic approach for seismic microzonation integrating 3D geological and geotechnical uncertainty
Bibliographic record
Abstract
A novel probabilistic methodology for regional seismic site characterization is proposed and applied to a region with highly heterogeneous surficial geology and varying soil sediment thickness and stiffness. The method combines various sources of geological and geotechnical uncertainties to develop a three‐dimensional (3D) shear‐wave velocity ( V s ) model and evaluate the associated uncertainties. A 3D geological model of the unconsolidated deposits was developed using geostatistical interpolation and simulation methods. Sequential indicator simulations produced a quantitative geologic model that explicitly quantified geological uncertainties based on the likelihood of specific soil types occurring. In situ measurements and multivariate statistical analysis allowed the development of empirical correlations between V s , geotechnical parameters, depth, and soil types. The resulting 3D V s values were estimated on the basis of V s ‐depth correlations and the probability of occurrence of each soil type. In this approach, the propagated uncertainty was also quantified by considering the combined variance. Seismic microzonation mapping was then conducted by transforming the 3D V s model into two‐dimensional (2D) maps that represent the spatial distributions of the time‐averaged shear‐wave velocity of the top 30 m ( V s , 30 ) and the fundamental site period ( T 0 ), along with their respective uncertainties using Monte Carlo simulations. The results indicate that microzonation maps and their uncertainties are influenced by the thickness, occurrence probability, and geotechnical properties of soils. The proposed method can be used to assess the probabilistic seismic risk at local and regional scales in areas with geologically and geotechnically complex soil properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".