High-performance computing and visualization of earthquake simulations and ground-motion sensor network data

Tung-Ju Hsieh; Shiann-Jong Lee; Yuan-Sen Yang; Yang-Lang Chang; Bormin Huang; Cheng-Kai Chen; Kwan-Liu Ma

doi:10.1117/1.JRS.6.061501

3 April 2012 High-performance computing and visualization of earthquake simulations and ground-motion sensor network data

Tung-Ju Hsieh, Shiann-Jong Lee, Yuan-Sen Yang, Yang-Lang Chang, Bormin Huang, Cheng-Kai Chen, Kwan-Liu Ma

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 6, Issue 1, 061501 (April 2012). https://doi.org/10.1117/1.JRS.6.061501

Abstract

Comparing numerical simulation results with accelerograph readings is essential in earthquake investigations and discoveries. We provide a case study on the magnitude 7.6 Taiwan Chi-Chi earthquake in 1999. More than 400 seismic sensor stations recorded this event, and the readings from this event increased global strong-motion records fivefold so that the accuracy of the earthquake simulation was enhanced significantly. Direct volume rendering is used to depict the space-time relationships of numerical results and seismic readings. When earthquake simulation data are volume rendered, it reveals the sequence of seismic wave initiation, propagation, attenuation, and energy releasing events of fault ruptures so that the direction of seismic wave propagation can be observed. Both accelerograph readings and earthquake simulation data are used to generate a sequence of ground-motion maps. Stacking these maps up in sequence forms a volume data. Visual analysis of the time-varying component reveals hidden features for better comparison and evaluation. Earthquake scientists are able to obtain insights and evaluate their simulation criteria from volume rendering.

Citation Download Citation

Tung-Ju Hsieh, Shiann-Jong Lee, Yuan-Sen Yang, Yang-Lang Chang, Bormin Huang, Cheng-Kai Chen, and Kwan-Liu Ma "High-performance computing and visualization of earthquake simulations and ground-motion sensor network data," Journal of Applied Remote Sensing 6(1), 061501 (3 April 2012). https://doi.org/10.1117/1.JRS.6.061501

Published: 3 April 2012

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