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Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data
Takahiro Maeda† and Hiroyuki Fujiwara
National Research Institute for Earth Science and Disaster Resilience
3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan
†Corresponding author
Received:October 25, 2016Accepted:February 7, 2017Online released:March 16, 2017Published:March 20, 2017
Keywords:long-period ground motion, visualization, simulation, parallel distributed processing, clustering
Abstract
This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually; they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.
Cite this article as: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.Data files:
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