Geospatial Big Data-Based Geostatistical Zonation of Seismic Site Effects in Seoul Metropolitan Area

Abstract

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.

Geospatial Assessment of the Post-Earthquake Hazard of the 2017 Pohang Earthquake Considering Seismic Site Effects

“…In addition, weathered soil and rock exposed to long-term weathering, was also found in the target area and evenly distributed up to 20.2 m. Such zones involving thick soil and the bedrock at a large depth are susceptible to ground motion amplification. To determine reliable site-specific criteria for geotechnical layer classification considering the local site effects, the cross-validation-based root mean square error (RMSE) for geo-layers was calculated as 1.2 m on average [12]. For comparison, the RMSE of the cross-validation result was the square root of the average squared distance of a data point from the fitted line, as calculated with the following equation:…”

“…In this study, the geospatial grid information, constructed by the collected geo-data, was used as the base value for seismic site classification. geo-layers was calculated as 1.2 m on average [12]. For comparison, the RMSE of the cross-validation result was the square root of the average squared distance of a data point from the fitted line, as calculated with the following equation:…”

GIS-Based Optimum Geospatial Characterization for Seismic Site Effect Assessment in an Inland Urban Area, South Korea

“…The KDE approach is a non-parametric procedure that uses a technique to estimate spatial density using a kernel function to fit a smoothly tapered surface to each point [47,48]. The kernel density represents a smooth and continuous surface map because interpolation generates a continuous discrete pattern [49]. The kernel calculator can be defined for a particular set of observations from an unknown probability density function:…”

Geospatial Data Management Research: Progress and Future Directions

“…The massive use of geo-referenced data sets in many fields of science and economy including earth observation [1], environmental sciences [2], city planning [3], BIM [3,4], real-time processing [5,6], and analytics for geospatial data [5] makes geospatial data management increasingly a central task in the workflow of geospatial data processing [1,5,7]. Recent approaches consider open data platforms and containers to handle big geospatial raster and vector data efficiently.…”