Title

Artificial neural networks to represent the attenuation of seismic intensity

Document Type

Article

Publication Date

11-4-2013

Publication Title

Ingenieria y Universidad

Abstract

The study of seismic intensity attenuation plays an important role in the analysis of menace that includes historical events. Mapping the intensity attenuation is usually done by regressing the intensity versus distance. Nowadays there are different ways to examine the characteristics of a seismic event using instrumental means, however, the experts face the problem of qualitative nature of the sources of information and the mapping of the relationship between intensity, magnitude and distance for the generation of risk scenarios based on historical information. This paper presents an alternative to map this relationship through artificial neural networks (ANN). As a result, we propose a procedure that was validated through the mapping of the intensities of 68 earthquakes that occurred northern South America, between 1766 and 2004. We found that ANNs present advantages with respect to the conventional models of regression: a. they preserve in a better way the first statistical moment, b. they reflect a minor approximation error and c. the variance explained by the ANN is better than the one from the models of statistical regression.

Volume

17

Issue

2

First Page

277

Last Page

292

ISSN

01232126

Identifier

SCOPUS_ID:84886645209

Documento original

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