Through the cluster analysis, this study shows that this algorithm can help us to identify the tectonic blocks at a large scale. Based on the statistical results, I can separate the island of Taiwan into four domains and the boundaries of four blocks are generally along the Longitudinal Valley, Western Foothills and Eurasian continental shelf, which represents different tectonic movements across the suture of arc-continent collision, the deformation front, and southwestward movement of Pingtung Plain likely associated with tectonic extrusion. Also, I used the cluster analysis for four domains including the northern Taiwan, the western Taiwan, the eastern Taiwan, and the southwestern Taiwan.
The clustering result for each area is also similar to the clustering result for whole Taiwan.
In addition to dividing Taiwan into four blocks through the statistical algorithms, I also increase the number of clusters to observe the trend of the clustering result. When k
= 10, the island of Taiwan can be separated into ten blocks including the Western Foothills, Ilan Plain, east and west of the Longitudinal Valley fault, north and south section of the Central Range, scatter clusters along the Western Foothills, Chuchiungkeng-Muchiliao fault, Chungchou fault, the clusters between the Kaoping River structure and the Chaochou structure, and west of the Fengshan structure. When the number of clusters increases, it can still reveal some tectonic patterns until k = 34. For k > 34, the clusters
will have either few data points, which may not be representative for the block movement, or distributed points, which may not be correlated in the geographic space. In the process of clustering, it is still having some clusters cannot fit well with the known active structures.
The result shows that the order of the clusters is somewhat relevance to the slip rates of the active faults and the seismic potential of these structures. It also can be regard as another evidence to suggest the existence of inferred active structures. Besides, earlier determined clusters represent higher velocity gradient between the clusters, which may indicate active faults with higher slip rates. The cluster analysis offers a simple, fast, and visual tool to help the researcher to gain a preliminary understanding of a region without any prior geological assumptions.
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