Session:

LBP - Late Breaking Papers

Title:

An Application of Evolutionary Algorithms to Predict the Extent of SLHF Anomaly Associated with Coastal Earthquake

Authors:

Guido Cervone
Liviu Panait
Ramesh Singh
Menas Kafatos
Sean Luke

Abstract:

Multi sensor remote sensing provides real time high resolution data that can be used to study anomalous changes on land, in the ocean, and in the atmosphere associated with an impending earthquake. Anomalous behaviour in Surface Latent Heat Flux (SLHF) prior to large coastal earthquakes has been recently found. However, an SLHF time series usually contains several sharp peaks that may be associated either with earthquakes or with atmospheric perturbations. In this paper we have used evolutionary algorithms to perform a search in a large space bounded by longitude, latitude and time, to distinguish between signals associated with earthquakes and those associated with atmospheric phenomena. The algorithm finds paths which delimit the extent of the detected anomalies by optimizing an objective function that takes into consideration several aspects, such as spatial and time continuity, the magnitude of the anomalies, and the distance to the continental boundary. This search strategy is crucial for the development of a fully automated early warning system for providing information about impending earthquakes in a seismically active coastal region. Experiments have been performed over a 2000 km^2 area comprising a part of the continental boundary between the African and Eurasian plate, roughly corresponding to Italy and Greece, one of the most seismically active regions. Using a 365-days-long time series, we identified three signals associated with seismic events. Additionally, it was possible to establish that the extent of the signal does not propagate further than 600 km from the epicenter of the earthquake.