The Prediction of the Earthquake Trend in Taiwan and Sichuan Region Based on the Neutral Network Model and Seismic Change Rate

Wei Li; Shao Ying Huang; Jian Feng Qiu; Xing Chang Wang

doi:10.4028/www.scientific.net/AMR.204-210.994

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 204-210The Prediction of the Earthquake Trend in Taiwan...

The Prediction of the Earthquake Trend in Taiwan and Sichuan Region Based on the Neutral Network Model and Seismic Change Rate

Abstract:

We recently proposed a technique able to analyze the trend of the seismic activity by combining neural network model and seismic factors. In this paper, a variation of seismicity was introduced to reflect the corresponding vary of the frequency of earthquake. This variation was used as the precursor of future moderate earthquake. The time intervals of earthquake will be obtained through training the neural network. Then we judge the occurrence of strong earthquakes according to the time intervals. Finally, some statistical researches were made by using the method for the earthquake catalog in Sichuan and Taiwan in China. Through this above, we verify the validity of this method and state the general steps of this method.

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Periodical:

Advanced Materials Research (Volumes 204-210)

Pages:

994-999

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.204-210.994

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Online since:

February 2011

Authors:

Wei Li, Shao Ying Huang, Jian Feng Qiu, Xing Chang Wang

Keywords:

Neural Network Model, Trend of Earthquake, Variation of Seismicity

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References

[1] Du li, Chen tao, Du yan and Zhou qingxin: BP Network's Simulation and analysis for predicting [J]. Learn Newspapers of Kunming University of Science (Science & Engineering), 2003, (05).

Google Scholar

[2] H. Adeli and S.L. Hung, Machine Learnig-Neural Networks, Genetic Algorithms and Fuzzy System(Wiley, New York, NY, 1995).

Google Scholar

[3] Y.Y. Kagan, VAN earthquake predictions, a statistical evaluation, Geophysical Journal International 104(1991)117-133.

Google Scholar

[4] T. kerh, and D. Chu, Neural networks approach and micro-tremor mesurements in estimating peak ground acceleration due to strong motion, Advances in Engineering Software 33(11)(2002)733-742.

DOI: 10.1016/s0965-9978(02)00081-9

Google Scholar

[5] R. Leach and F. Dowla, Earthquake early warning system using real-time signal processing, Neural Networks for Signal Processing, Proceedings of the 1996 IEEE Signal Processing Society Workshop, September4-6, Kyoto, Japan(1996), pp.463-472.

DOI: 10.1109/nnsp.1996.548376

Google Scholar

[6] Bertalan Bodri. A neural-network model for earthquake occurrence.J. Journal of Geodynamics 32, (2001). p.289–310.

DOI: 10.1016/s0264-3707(01)00039-4

Google Scholar