{"title":"Evaluation of a geospatial liquefaction model using land damage data from the 2016 Kaikōura earthquake","authors":"Amelia Lin, L. Wotherspoon, J. Motha","doi":"10.5459/bnzsee.55.4.199-213","DOIUrl":null,"url":null,"abstract":"The paper uses two geospatial liquefaction models based on (1) global and (2) New Zealand specific variables such as Vs30, precipitation and water table depth to estimate liquefaction probability and spatial extent for the 2016 Kaikōura earthquake. Results are compared to observational data, indicating that the model based on global variables underestimates liquefaction manifestation in the Blenheim area due to the low resolution of the input datasets. Furthermore, a tendency for underprediction is evident in both models for sites located in areas with rapidly changing elevation (mountainous terrain), which is likely caused by the low resolution of the elevation-dependent variables Vs30 and water table depth leading to incorrect estimates. The New Zealand specific model appears to be less sensitive to this effect as the variables provide a higher resolution and a better representation of region specific characteristics. However, the results suggest that the modification might lead to an overestimation of liquefaction manifestation along rivers (e. g. Kaikōura). An adjustment of the model coefficients and / or the integration of other resources such as geotechnical methods can be considered to improve the model performance. The evaluation of the geospatial liquefaction models demonstrates the importance of high resolution input data and leads to the conclusion that the New Zealand specific model should be preferred over the original model due to better prediction performance. The findings provide an overall better understanding on the models’ applicability and potential as a tool to predict liquefaction manifestation for future hazard assessments.","PeriodicalId":46396,"journal":{"name":"Bulletin of the New Zealand Society for Earthquake Engineering","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of the New Zealand Society for Earthquake Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5459/bnzsee.55.4.199-213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The paper uses two geospatial liquefaction models based on (1) global and (2) New Zealand specific variables such as Vs30, precipitation and water table depth to estimate liquefaction probability and spatial extent for the 2016 Kaikōura earthquake. Results are compared to observational data, indicating that the model based on global variables underestimates liquefaction manifestation in the Blenheim area due to the low resolution of the input datasets. Furthermore, a tendency for underprediction is evident in both models for sites located in areas with rapidly changing elevation (mountainous terrain), which is likely caused by the low resolution of the elevation-dependent variables Vs30 and water table depth leading to incorrect estimates. The New Zealand specific model appears to be less sensitive to this effect as the variables provide a higher resolution and a better representation of region specific characteristics. However, the results suggest that the modification might lead to an overestimation of liquefaction manifestation along rivers (e. g. Kaikōura). An adjustment of the model coefficients and / or the integration of other resources such as geotechnical methods can be considered to improve the model performance. The evaluation of the geospatial liquefaction models demonstrates the importance of high resolution input data and leads to the conclusion that the New Zealand specific model should be preferred over the original model due to better prediction performance. The findings provide an overall better understanding on the models’ applicability and potential as a tool to predict liquefaction manifestation for future hazard assessments.