Assessment of Arias Intensity of historical earthquakes using modified Mercalli intensities and artificial neural networks

G-A Tselentis

doi:10.5194/nhess-11-3097-2011

Abstract

Abstract. This paper presents the development of a non-parametric forecast model based on artificial neural networks for the direct assessment of Arias Intensity corresponding to a historic earthquake using seismic intensity data. The neural models allow complex and nonlinear behaviour to be tracked. Application of this methodology on earthquakes with known instrumental data from Greece, showed that the artificial neural network forecast model have excellent data synthesis capability.

Highlights

With widespread use of strong motion recorders, it is possible to obtain engineering seismic parameters (ESP), such as Arias Intensity (Ia ) for the majority of strong earthquakes that occur today
Since most loss-estimation methodologies in use today (Kaestli et al, 2006), are based on the distribution of ESP, the task of forecasting these parameters from seismic intensity is very important to quantify the effect of historical earthquakes for which no instrumental data are available
In the present investigation by seismic intensity, we refer to the Modified Mercalli Intensity scale (MMI)

Summary

Introduction

With widespread use of strong motion recorders, it is possible to obtain engineering seismic parameters (ESP), such as Arias Intensity (Ia ) for the majority of strong earthquakes that occur today. Many investigators have compared these parameters to seismic intensity data, but have found that the correlation is usually poor and the relationships are highly nonlinear in nature (e.g., Tselentis and Vladutu, 2010).

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Conclusion