Prediction of Aftershocks Distribution Using Artificial Neural Networks and Its Application on the May 12, 2008 Sichuan Earthquake
R. Madahizadeh and M. Allamehzadeh
In this paper an approach is presented to predict the concentration and the trend of aftershocks of May 12 2008 Chengdu, Sichuan, China earthquake. The method is based on inputting first aftershocks to Kohonen artificial neural network. Artificial neural networks, which are inspired from human brain, consist of several artificial neurons which are connected with some weight vectors to each other. Artificial neural networks are able to classify a large volume of input data (i.e. earthquake catalogue) simultaneously and in parallel, and can recognize seismic patterns very well. Kohonen neural networks consist of several neurons that affect mutually on each other to display important statistical characteristics of the input space (i.e. first aftershocks). Combination of associative and competitive learning rules results in formation of Kohonen’s self-organizing feature map (SOFM) algorithm. SOFM algorithm has converged; the feature map computed by the SOFM algorithm indicates the concentration and the trend of aftershocks precisely. Kohonen artificial neural networks have become powerful intelligent tools in recent years, used widely in pattern recognition and data clustering.
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Fall 2009, Vol 11, No. 3
/در مجله JSEE /توسط adminPrediction of Aftershocks Distribution Using Artificial Neural Networks and Its Application on the May 12, 2008 Sichuan Earthquake
R. Madahizadeh and M. Allamehzadeh
In this paper an approach is presented to predict the concentration and the trend of aftershocks of May 12 2008 Chengdu, Sichuan, China earthquake. The method is based on inputting first aftershocks to Kohonen artificial neural network. Artificial neural networks, which are inspired from human brain, consist of several artificial neurons which are connected with some weight vectors to each other. Artificial neural networks are able to classify a large volume of input data (i.e. earthquake catalogue) simultaneously and in parallel, and can recognize seismic patterns very well. Kohonen neural networks consist of several neurons that affect mutually on each other to display important statistical characteristics of the input space (i.e. first aftershocks). Combination of associative and competitive learning rules results in formation of Kohonen’s self-organizing feature map (SOFM) algorithm. SOFM algorithm has converged; the feature map computed by the SOFM algorithm indicates the concentration and the trend of aftershocks precisely. Kohonen artificial neural networks have become powerful intelligent tools in recent years, used widely in pattern recognition and data clustering.
Full Paper