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On-chip earthquake simulation model using potentials

Author

Listed:
  • I. Georgoudas
  • G. Sirakoulis
  • E. Scordilis
  • I. Andreadis

Abstract

A two-dimensional (2-D) Cellular Automata (CA) dynamic system constituted of cells-charges has been proposed for the simulation of the earthquake process. The CA model has been calibrated with the use of real data. The calibration incorporates major seismic characteristics of the area under test. The simulation results are found in good quantitative and qualitative agreement with the recorded Gutenberg–Richter (GR) scaling relations. The model is enriched with a powerful multi-parameter interface that enables the user to load real data from different regions. This paper examines the on-chip realisation of the model and its instrumentation. The CA model hardware implementation is based on Field Programmable Gate Array (FPGA) logic. It utilises an array of 32 × 32 cells. Parameters that construct the local CA rule constitute the input data. The initial seed, which to some extent corresponds to the seismic features of the area under test, is loaded in a semi-parallel way and the process is completed in a certain number of time steps. The automatic response of the processor provides the corresponding GR scaling law of the area under study. The hardware implementation of the CA-based earthquake simulation model is advantageous in terms of low-cost, high-speed, compactness and portability features. It can operate as a preliminary data-acquisition filter that accelerates the evaluation of recorded data as far as its origin time, spatial and magnitude completeness and quality are concerned. Software that performs reliable automatic phase picking, as well as data elaboration, can be assembled next to the earthquake recording instruments (the whole network) output to assure a quick and reliable iteration of the focal parameters of a recorded earthquake (epicentre coordinates, focal depth and magnitude). The dedicated processor can be accommodated right after this stage (before any manual elaboration) focusing on the near real-time development of a reliable qualitative dynamical seismic record and a mapping of the seismic characteristics of the area. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • I. Georgoudas & G. Sirakoulis & E. Scordilis & I. Andreadis, 2009. "On-chip earthquake simulation model using potentials," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(3), pages 519-537, September.
    • Handle: RePEc:spr:nathaz:v:50:y:2009:i:3:p:519-537
    DOI: 10.1007/s11069-008-9255-1
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    References listed on IDEAS

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    1. Salvatore Alparone & Daniele Andronico & Tiziana Sgroi & Ferruccio Ferrari & Luigi Lodato & Danilo Reitano, 2007. "Alert system to mitigate tephra fallout hazards at Mt. Etna Volcano, Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 43(3), pages 333-350, December.
    2. G. Crisci & S. Di Gregorio & R. Rongo & W. Spataro & F. Nicoletta, 1999. "Analysing Lava Risk for the Etnean Area: Simulation by Cellular Automata Methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 215-229, November.
    3. Bruce Malamud & Donald Turcotte, 1999. "Self-Organized Criticality Applied to Natural Hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 93-116, November.
    4. Hernandez, Gonzalo, 2002. "Parallel and distributed simulations and visualizations of the Olami–Feder–Christiensen earthquake model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 301-311.
    5. P. Dimitriu & E. Scordilis & V. Karacostas, 2000. "Multifractal Analysis of the Arnea, Greece Seismicity with Potential Implications for Earthquake Prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 21(2), pages 277-295, May.
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