Author:
Maeda Takahiro, ,Fujiwara Hiroyuki
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.
Publisher
Fuji Technology Press Ltd.
Subject
Engineering (miscellaneous),Safety, Risk, Reliability and Quality
Reference9 articles.
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. 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, 2016.
3. J. B. MacQueen, “Some Methods for classification and Analysis of Multivariate Observations,” Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol.1, University of California Press, pp. 281-297, 1967.
4. J. Ward, “Hierarchical grouping to optimize an objective function,” J. Amer. Statist. Assoc., Vol.58, pp. 236-244, 1963.
5. http://spark.apache.org/ [accessed Oct. 25, 2016]
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献