Discussion

In this study, IDW model and six models were tested. First of all. IDW method were applied. The velocity obtained large change in different depths. The main reason is that the original IDW do not promise the sum of weights is one. If it is less searched points, the interpolation obtain worse result. As an interpolation method, the benefits of IDW are simple and fast. But the weight function does not have various shape to fit spatial relation well.

To combine the four types of data, the confidence weight factor of different approaches were considered. Differ from traditional interpolation methods, this method do not regard all types of data as the same type. Therefore the velocity results

-0.04

68

measured by the microtremor array, microtremor H/V, received function were simulated. The simulated result were used to be the confidence weight factors. The method A gave averaged values to each data with the same type. And the method B gave the results at each site to each data with the same type. Namely, method A mainly based on data types, and method B based on sites. Due to the reason the characteristics of sites vary greatly generally, it is considered that the method B should have the better simulated result rather than method A.

The ordinary kriging has many parameters. The key to parameters of the ordinary kriging is the semivariogram. In this study, semivariograms were calculated by three scales of depth. The first reason is that the plethora of data makes the calculation complex. Besides, because EGDT data distributed at shallower depth with high density, the spatial relation could be controlled by EGDT data. Finally larger number of data do not guarantee the best fit for a spatial relation. In contrast, the better spatial relation derived from the spatial representative data.

The six models built by different scenarios and different processed data were tested by ME, MAE and MAPE. Model A, B, and C get small error results than model D, E, and F. There are some reasons that make it such difference. The first reason is that model D, E, and F considered both horizontal and vertical semivariograms. It makes the number change greater than only consider horizontal one. Some received data at deep locations have smoother change. They do not fit the vertical

semivariogram well. Besides the confidence weight factor changes the weight of ordinary kriging. It causes that if larger confidence weight factors were given to

69

points at simulated location, the small error should be get. In contrast, the smaller confidence weight factors make the worse cross-validation results. In this study, confidence weight factors with small difference were given, the results of cross-validation do not get large difference.

Compared with the upper, middle and lower depth scales of models, some trend can be observed. The ME increases with depth. It is that the observed points at lower part are less than the middle and upper parts. Namely, the uncertainty increases with depth. The MAE presents the degree of dispersion. The middle part of model A, B and C get less the MAE than the upper and lower parts. In contrast, the MAE of model D, E and F increases with depth. In addition, the MAPE can be considered as one of the precise of estimation. The result shows that the upper get greater error than the middle and lower parts. It concerned about the observed value. The observed value in the upper usually smaller than observed value in the middle and lower parts. This make the MAPE in upper part larger than the others.

The simulation result shows that the scenarios considered both horizontal and vertical semivariograms get better simulation results. Next, models built by these scenarios get larger difference to each other. However, it can not observe the obvious difference in model A, B, and C. these models only used the horizontal

semivariogram. Their value change less than the models used both horizontal and vertical semivariograms. In the model D, E, and F, the fitness of model E is the lowest. Conversely, model F obtained the highest fitness. Model E used the mean fitness of each method to be the confidence weight factor values. The worse fitness

70

may result from these simplified values. In contrast, the confidence weight factors of model F determined by each site and method obtained better but limited

improvement.

Appendix F shows the profiles of interpolated results. The model A, B, and C are similar with each other. In contrast, the model D, E, and F are similar as well. Some discontinuous S-wave velocity can be found between 200 and 700 m depth in all models. The discontinuous S-wave velocity location may concerned about the

measure error or detecting different materials. The results shows the Taipei Basin is a triangular alluvium clearly. Secondly, the deepest location in the Taipei Basin is located at the west of Taipei city. Finally, the shallow layers with low S-wave velocity can be observed, especially at the latitude 2775000. It reflects the observed Wugu, Jingmei and Songshan layers. Figure 4.11 shows the S-wave velocity profile and Figure 4.12 depicts profile line of Figure 4.11. These two figures can correspond to Figure 2.2 well.

71

Figure 4.11 The S-wave velocity profile built by model D.

Figure 4.12 The Profile line of figure 4.11.

72