The basin effect and liquefaction in the catastrophe models: Case study – Vancouver region, Canada
Bibliographic record
Abstract
The presence of thick soft alluvial sediment-filled basins, like in river’s deltas, can significantly amplify and prolongate the earthquake ground motion. Moreover, the high-water saturation of such soft sediments and cyclic earthquake loading can lead to liquefaction. The basin and liquefaction effect can contribute to substantial modification of the seismic motion and increase of the potential losses at a particular location. Well-known examples of such high financial losses during earthquakes for basin effect is Mw 8.1 Mexico City 1985 and for liquefaction is Darfield and Christchurch earthquakes series in 2010 and 2011. Thus, the quantification of these effects is particularly important for the current underwriting products and the industry requires their further detailed consideration in the catastrophe models and pricing approaches. Impact Forecasting, Aon’s catastrophe model development center of excellence, has been committed to help (re)insurers on that matter. This paper presents case study of the quantification of the basin effect and liquefaction for Vancouver region, Canada for specific scenario Mw 7.5 Strait of Georgia crustal earthquake. The southern part of the Vancouver region is located on a deep sedimentary basin created in the Fraser River delta. In case of deep Vancouver sedimentary basin considering amplification only due to shallow site response Vs30-dependent site term is not sufficient. Therefore, we derived (de)amplification function for different periods to quantify basin effect. We used NGA – West 2 ground motion prediction equations (GMPEs) for crustal events which include basin depth term. Amplification function was derived with respect to standard GMPEs for crustal events in western Canada. Amplification, considering site response including Vs30 and basin depth term at period 0.5 s can reach values as high as 3 at the softest and deepest sediments. The liquefaction potential was based on HAZUS and Zhu et al. (2017) methodologies calibrated to better reflect local geological conditions and liquefaction observations (Monahan et al. 2010, Clague 2002). We used USGS Vs30 data, enhanced by local seismic and geologic measurements, to characterize soil conditions, and topographical data and IF proprietary flow accumulation data to characterize water saturation. Liquefaction hazard is calculated in terms of probability of liquefaction occurrence and permanent ground deformation. For the chosen scenario the potential contribution to mean loss due to basin effect could be in the range 15% - 30% and 35% - 75% due to liquefaction depending on structural types of the buildings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".