## 3 Geospatial Informatics of the Geotechnical Database
### Geospatial Interpolation
In the case of average \(N_{60}^{exp}\), the southern and northeastern regions have considerably higher \(N_{60}^{exp}\) (>250), where mountains and hills are located. These regions were also classified as cold spots and have lower \(K\). Root mean square errors (RMSEs) based on cross-validation were calculated for the best-fitting model. The sequential value at each sampling point was calculated by computing an experimental  
Figure 8: Correlations between measured and predicted geo-layer thicknesses and correlations with measured and predicted average \(N_{60}^{exp}\). The linear relations between measured and predicted values are shown by the dotted lines and summarized as tables. The residuals of prediction are shown by the blue lines. variogram and a kriging model, after exclusion of the measured target values at a given point. Differences between the predicted and measured values (geo-layer thickness and average \(N_{60}^{exp}\)) were compared, and prediction residuals were computed for each borehole dataset (Figure 8). Geo-layers with shallower depths (i.e., closer to the surface) and lower \(N_{60}^{exp}\) showed greater fluctuations in the prediction residuals. This is because values in shallower geo-layers with low soil stiffness are concentrated. For the interpolations of thickness, the average residuals ranged between \(-5\) and \(-5\). On the contrary, the average residuals of average \(N_{60}^{exp}\) ranged between \(-100\) and \(-100\). The ratio between the measured and predicted values was derived based on linear regression. The IDW technique showed better predictions, with an approximate ratio of 1:1 with measurements. Among the kriging methods, SK estimated similar values with measurements compared to OK and EBK. The predictions became more reliable as RMSE approached zero in the following order: AS, FL, WS, and WR. Most boreholes were drilled on plains with lower morphologic variability (deviation of elevation \(<5\) m; deviation of slope \(<1^{\circ}\)), where soil deposits were accumulated. Accordingly, there were numerous variants of AS and FL, and where sampling is quite regular, and spatial correlations are strong the prediction reliability was improved. This study focuses on demonstrating that site-specific geometry developed using IDW is a powerful predictor of the locations of thin soil deposits with low stiffness. Thus, stochastic correlations between the upper boundary depth of each geo-layer and the average \(N_{60}^{exp}\) were estimated based on the grouped mean \(N_{60}^{exp}\) on unit depth assigned in the interpolated multiscale grid (53,791 cells) (Figure 9). For FL, the deeper the depth, the greater the \(N_{60}^{exp}\). For AS, \(N_{60}^{exp}\) increased to approximately 7 m depth, after which it decreased. The depth variation of \(N_{60}^{exp}\) for the weathered layer was not significant. When only a subsurface stratum is determined, \(N_{60}^{exp}\) can be predicted using the regression equations for each geo-layer type and thickness. Figure 9: Box plots of correlations between grid-based predicted geo-layer depth and average \(N_{60}^{exp}\): (**a**) fill layer; (**b**) alluvial soil; (**c**) weathered soil; (**d**) weathered rock. The blue dotted lines show the regression line and equations with the highest \(R^{2}\).