Development of a probabilistic seismic microzonation software considering geological and geotechnical uncertainties
Bibliographic record
Abstract
Modern seismic risk assessment software includes seismic hazard analysis algorithms or relies on precomputed hazard maps. In both cases, a major step is incorporating the impacts of the local geological and geotechnical conditions. The common practice to address this phenomenon, often referred to as site effect, is to use shear wave velocity (Vs)–depth correlations and a deterministic geological model as a proxy for Vs mapping. The Vs of the top 30 m (Vs30) and often the fundamental site period (T0) are then used as predictors of the potential amplification of seismic shaking. Recognising that uncertainty in local soil properties inevitably affects the seismic site response, a stochastic approach for evaluating Vs30 and T0 is proposed herein, considering a combination of probabilistic Vs–depth correlations and a probabilistic 3D geological model. Monte Carlo simulations are then applied to study the impact of the uncertainties on the seismic site characterisation model. The generated stochastic maps consist of Vs30 and T0 realisations accompanied with spatial distribution of uncertainty as an indication of areas where further fieldwork is needed to improve predictions. The developed methodology is currently being used in a homemade risk assessment software.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".