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Artificial Neural Network Methodology for Three-Dimensional Seismic Parameters Attenuation Analysis

  • Ben-yu Liu
  • Liao-yuan Ye
  • Mei-ling Xiao
  • Sheng Miao
  • Jing-yu Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

With the accumulating of the strong earthquakes records, it becomes practicable to achieve the more accurate attenuation relationships. Based on the seismic records of West American, the Radial Basis Function (RBF) and Back Propagation (BP) artificial neural networks model are respectively constructed for three-dimensional seismic parameters attenuation relationship. The RBF model is nice fitting for the training data, although it has great errors on other tested points. While the BP model is not good than the RBF model for the training data, it possesses a better consecutive property in the whole area. It is a proper neural network model for the problem. After training with the selected records, the Neural Networks (NN) shows a good fitting with the training records. And it is easy to construct three-dimensional model to predict the attenuation relationship. In order to demonstrate the efficiency of the presented methodology, the contrast is discussed for the results of the BP model and three typical traditional attenuation formulae.

Keywords

Artificial Neural Network Radial Basis Function Artificial Neural Network Model Peak Ground Acceleration Back Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ben-yu Liu
    • 1
  • Liao-yuan Ye
    • 1
  • Mei-ling Xiao
    • 1
  • Sheng Miao
    • 1
  • Jing-yu Su
    • 2
  1. 1.Institute of Public Safety and Disaster PreventionYunnan UniversityKunmingChina
  2. 2.Civil Engineering DepartmentBeijing University of TechnologyBeijingChina

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