## 3 Results
### Comparison  
A comparison is made against the aforementioned HVSR studies. For simplicity, the model for Japan will be called GA14J, and the model for the world will be called GA14G [42]; the model for Central and Eastern North America will be called HA16 [43]; the model for California, United States, will be called HA18 [44]; and the model for Iran will be called YSH20 [45]. When comparing with\(f_{\rm P}\), the GA14J model seemingly does better than the GA14G model as shown in Figure 8a. For simplicity, without loss of generality, the figure only plots the acceleration dataset. It also plots the mean and \(\pm 1\) standard deviation for both models, with GA14J being in black and GA14G being in violet. The shaded portion in the figure is the \(\pm 1\) standard deviation of the current model and is shown for comparison purposes. Figure 6: Resultant\(f_{\rm P}\) and \(V_{530}\) for acceleration and velocity datasets when removing a minimum frequency condition. Regressed line for acceleration data is shown in bold with dashed lines being \(\pm 1\) standard deviation. This regression does not consider HVSR with \(f_{\rm P}\) < 1 Hz. Station HVSR characteristics that did not satisfy the \(A_{\rm P}\) conditions are shown in gray. Figure 8b shows residuals of the log \(V_{\rm S30}\) results. For brevity, only acceleration-based data are plotted, with velocity-based data showing similar behavior. Both plots suggest GA14J is a better fit relative to GA14G, although the model does not properly capture the aggregate slope of the local data. RMSE for GA14J is 0.14, while GA14G is 0.15. Similarly, Figure 9a shows the comparison between the HA16 and HA18 models. In this case, both models seem to make tradeoffs at low and high frequencies. Figure 9b presents the residuals of the log \(V_{\rm S30}\) results and shows an overall better fit by capturing the aggregate slope of the acceleration data relative to the GA14 models. Velocity data also show similar behavior. The HA18 model results in more data outside the standard deviation boundary, with RMSE for HA16 at 0.12 and for HA18 at 0.14. Figure 8: Comparison of GA14J and GA14G models to current dataset. (**a**) Plot of resultant \(f_{\rm p}\) and \(V_{\rm S30}\) for acceleration dataset. GA14J model is shown in black lines while GA14G model is shown in violet lines. Dashed lines represent \(\pm 1\) standard deviation of respective models. Shaded area is the \(\pm 1\) standard deviation of the current model and is shown for comparison; (**b**) Residuals between measured data and estimates from acceleration data. Dashed lines indicate \(\pm 1\) standard deviation of GA14 models. Again, shaded area is the \(\pm 1\) standard deviation of the current model and is shown for comparison. Figure 7: (**a**) Resultant \(f_{\rm p}\) and \(V_{\rm S30}\) for acceleration and velocity datasets when absolute peak HVSR amplitude is selected. Regressed line for acceleration data is shown in bold with dashed lines being \(\pm 1\) standard deviation; (**b**) Residuals between the logarithm of measured data and the logarithm of estimates from acceleration and velocity-based data regression models. Finally, Figure (a)a shows the comparison for the YSH20 model. The plot shows a significant mismatch to the local data, which is confirmed in the residuals plot shown in Figure (b)b. Velocity data also show similar behavior. Similar to the GA14 models, the YSH20 model does not sufficiently capture the aggregate slope behavior. RMSE for YSH20 is 0.18, the highest of all five models considered. Additionally, unlike other models, the GA14J and GA14G models include versions that consider \(A_{\text{p}}\) only as well as the combination of \(f_{\text{p}}\) and \(A_{\text{p}}\). For completeness, a comparison is also made, with the results summarized in Table 3. The table shows the RMSE of each  
Figure 10: Comparison of YSH20 model to current dataset. (**a**) Plot of resultant \(f_{\text{p}}\) and \(V_{\text{S30}}\) for acceleration dataset.