The 2017 Pohang earthquake (moment magnitude scale: 5.4) was South Korea’s second strongest earthquake in decades, and caused the maximum amount of damage in terms of infrastructure and human injuries. As the epicenters were located in regions with Quaternary sediments, which involve distributions of thick fill and alluvial geo-layers, the induced damages were more severe owing to seismic amplification and liquefaction. Thus, to identify the influence of site-specific seismic effects, a post-earthquake survey framework for rapid earthquake damage estimation, correlated with seismic site effects, was proposed and applied in the region of the Pohang earthquake epicenter. Seismic zones were determined on the basis of ground motion by classifying sites using the multivariate site classification system. Low-rise structures with slight and moderate earthquake damage were noted to be concentrated in softer sites owing to the low focal depth of the site, topographical effects, and high frequency range of the mainshocks.

Seismic site effects are influenced mainly by geospatial uncertainties corresponding to geological or geotechnical spatial variance. Therefore, the development of a geospatial database is essential to characterize site-specific geotechnical information in multiscale areas and to optimize geospatial zonation methods with potentially high degrees of spatial variability based on trial-and-error geostatistical assessments. In this study, a multi-source geospatial information framework, which included the construction of a big data platform, estimation of geostatistical density, optimization of the geostatistical interpolation method, assessment of seismic site effects, and determination of geospatial zonation for decision making, was established. Then, this framework was applied to the Seoul metropolitan area, South Korea. The GIS-based framework was established to develop the geospatial zonation of site-specific seismic site effects before considering the local characteristics of site effects dependent on topographic or geological conditions, based on a geospatial big-data platform in Seoul. The zonal conditions were composed of geo-layers, site effect parameters, and other multi-source geospatial maps for each administrative area, and infrastructure was determined based on the integration of the optimized geoprocessing framework.