We developed a clustering method combining principal component analysis and the k-means algorithm, which classifies earthquake scenarios based on the similarity of the spatial distribution of earthquake ground-motion simulation data generated for many earthquake scenarios, and applied it to long-period ground-motion simulation data for Nankai Trough megathrust earthquake scenarios. Values for peak ground velocity and relative velocity response at approximately 80,000 locations in 369 earthquake scenarios were represented by 15 principal components each, and earthquake scenarios were categorized into 30 clusters. In addition, based on clustering results, we determined that extracting relationships between principal components and scenario parameters is possible. Furthermore, by utilizing these relationships, it may be possible to easily estimate the approximate ground-motion distribution from the principal components of arbitrary sets of scenario parameters.

1. [1] T. Maeda and H. Fujiwara, “Seismic Hazard Visualization from Big Simulation Data: Construction of a Parallel Distributed Processing System for Ground Motion Simulation Data,” J. Disaster Res., Vol.11, No.2, pp. 265-271, 2016.
2. [2] T. Maeda and H. Fujiwara, “Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data,” J. Disaster Res., Vol.12, No.2, pp. 233-240, 2017.
3. [3] T. Maeda, A. Iwaki, N. Morikawa, S. Aoi, and H. Fujiwara, “Seismic-Hazard Analysis of Long-Period Ground Motion of Megathrust Earthquakes in the Nankai Trough Based on 3D Finite-Difference Simulation,” Seismological Research Letters, Vol.87, No.5, doi:101785/0220160093, 2016.
4. [4] H. Hotelling, “Analysis of a complex of statistical variables into principal components,” J. of Educational Psychology, Vol.24, pp. 417-441, 1933.
5. [5] J. B. MacQueen, “Some Methods for classification and Analysis of Multivariate Observations,” Proc. of 5^th Berkeley Symposium on Mathematical Statistics and Probability, 1, University of California Press, pp. 281-297, 1967.
6. [6] http://www.j-shis.bosai.go.jp/map/?lang=en [accessed Nov. 17, 2017]
7. [7] http://ecom-plat.jp/ [accessed Nov. 17, 2017]
8. [8] http://ecom-plat.jp/k-cloud/index.php [accessed Nov. 17, 2017]
9. [9] P. Wessel and W. H. F. Smith, “New version of the Generic Mapping Tools released,” Eos Transactions, American Geophysical Union, Vol.76, p. 329, 1995.