A Geospatial Model for Site Response Complexity
One-dimensional (1D) site response models assume vertically incident SH waves propagating through laterally constant soil layers. These assumptions, collectively referred to as the SH1D model, are widely used in site-specific ground motion predictions, although many studies have demonstrated their limitations. The term “site response complexity” (SRC) refers to the degree of discrepancy between the observed empirical transfer function (ETF) and the theoretical transfer function (TTF) computed with SH1D modeling. We present a geospatial approach to estimate site response complexity using statistical and machine learning methods with globally or regionally available geospatial proxies. Our site response data are from 114 vertical seismometer arrays in Japan’s Kiban-Kyoshin network (KiK-net) used in Kaklamanos and Bradley (2018). The SRC data are calibrated according to the Thompson et al. (2012) taxonomy that relies on two parameters, r (Pearson’s correlation coefficient between the ETF and TTF) and σi (inter-event variability of the ETF). We examine 18 geospatial proxies associated with site stiffness, topography, basin and saturation conditions. Using the geospatial proxies as explanatory variables, two sets of predictive models are developed: (a) linear regression models for predicting r and σi, separately; and (b) multiclass classification models for site response complexity. Our optimal SRC classification model uses the slope-based average shear wave velocity (VS30), global sedimentary deposit thickness, depth to shear wave velocity of 1.5 km/s (Z1.5) and global water table depth as explanatory variables. We generate maps across Japan for r, σi and SRC class, which can provide first-order approximations of site response complexity and exhibit clear patterns between SRC class and topography. LG sites are mostly located in flat sedimentary basins, LP sites are often located near mountain/basin edges, and H sites are located within mountainous areas. We conclude that a geospatial model for site response complexity is a promising first-order estimate of complexity in site response across broad regions.
Session: Advances in Geospatial Modeling of Seismic Hazards
Type: Oral
Room: Grand C
Date: 4/20/2022
Presentation Time: 02:45 PM Pacific
Presenting Author: Weiwei Zhan
Student Presenter: No
Additional Authors
Weiwei Zhan Presenting Author Corresponding Author weiwei.zhan@tufts.edu Tufts University |
Laurie Baise laurie.baise@tufts.edu Tufts University |
James Kaklamanos kaklamanosj@merrimack.edu Merrimack College |
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A Geospatial Model for Site Response Complexity
Category
Advances in Geospatial Modeling of Seismic Hazards
Description