A chilean seismic regionalization through a Kohonen neural network
Abstract
A study of seismic regionalization for central Chile based on a neural network is presented. A scenario with six seismic regions is obtained, independently of the size of the neighborhood or the reach of the correlation between the cells of the grid. The high correlation between the spatial distribution of the seismic zones and geographical data confirm our election of the training vectors of the neural network.
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