本研究目的在測試並提出結合既有的地球物理探勘資料的方法，並建立可用於地動模擬的臺北盆地三維速度構造模型。本研究結合了鑽探井測、微地動陣列、微地動單站頻譜比法以及接收函數法等資料，利用反距離權重法與普通克利金法分別進行測試。
在普通克利金法之中，測試了僅考量水平半變異函數的方法以及同時考量垂直與水平半變異函數兩種主要設定，並分別對該兩種設定的普通克利金法分別加入了所提出之信心權重因子方法進行測試結果。本研究測試了兩種信心權重因子，其中，第一種權重因子以方法類別為基礎，利用各方法之強震站模擬之平均值作為權重因子，第二種信心權重因子則以在相異場址之下，不同的方法模擬的個別結果作為依據。
本研究依照資料的空間分布將內插模型分為三個部分進行內插，以交叉驗證法驗證參數的結果，並以強震站單站頻譜法的模擬結果驗證模型用於模擬的適配性。其結果顯示反距離權重法得到的速度不符合實際地質情況，普通克利金法僅考量水平半變異函數使得內插數值變化較為緩慢，其交叉驗證的誤差略小於加入垂直向半變異函數的方法，加入信心權重因子對於結果變化並不明顯。相反地，考量垂直半變異數的方法對數值的影響變化較大，加入權重信心因子亦造成較大的數值變化。對兩項空間內插模型進行強地動單站頻譜法模擬，其結果顯示後者得到較好的適配性。

The purposes of this study is to test and propose a method combining different existing geophysical survey data and build a three-dimensional S-wave velocity model for ground motion simulation. The inverse distance weighting (IDW) and the ordinary kriging methods were applied to interpolate the data obtained from the suspension P-S logging, the microtremor array, the horizontal-to-vertical spectral ratio (H/V) and the receiver function approaches.
In regard to the ordinary kriging method, the test based on two types of setting, the first setting was to consider only the horizontal semivariogram to interpolate model. Conversely, the second was to consider both the horizontal and vertical semivariogram. Moreover, on the basis of the two major setting, the proposed confidence weight factor method was applied. Two set of the confidence weight factors calculated by the seismic H/V simulation result was tested, the first set based on the types of geophysical approaches. The second set based on considering the different approaches at each site.
According to the spatial distribution of data, three depth scales were divided in this study. The parameters were tested by cross-validation and the fitness of models were verified by the seismic H/V simulation. The result showed that the simulation of IDW method do not fit the real geological condition well. The value of the ordinary kriging considering both the horizontal and vertical semivariogram varied stronger than the other. And the effect of confidence weight factors was stronger as well. Despite the cross-validation result was worse than the method only considering horizontal semivariogram. The fitness of simulation was higher than the latter.

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