A Chilean seismic regionalization through a Kohonen neural network
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Through this paper we are presenting a study of seismic regionalization for continental Chile based on a neural network. A scenario with six seismic regions is obtained, irrespective of the size of the neighborhood or the range of the correlation between the cells of the grid. Unlike conventional seismic methods, our work manages to generate seismic regions tectonically valid from sparse and non-redundant information, which shows that the self-organizing maps are a valuable tool in seismology. The high correlation between the spatial distribution of the seismic zones and geological data confirms that the fields chosen for structuring the training vectors were the most appropriate.