Published on Tue Aug 19 2014

Can Artificial Neural Networks be Applied in Seismic Predicition? Preliminary Analysis Applying Radial Topology. Case: Mexico

Cinthya Mota-Hernandez, Luis Esquivel-Rodriguez, Rafael Alvarado-Corona

Tectonic earthquakes of high magnitude can cause considerable losses in terms of human lives, economic and infrastructure, among others. This document describes the development of an Artificial Neural Network based on the radial topology which seeks to generate a prediction with an error margin lower than 20%.

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Abstract

Tectonic earthquakes of high magnitude can cause considerable losses in terms of human lives, economic and infrastructure, among others. According to an evaluation published by the U.S. Geological Survey, 30 is the number of earthquakes which have greatly impacted Mexico from the end of the XIX century to this one. Based upon data from the National Seismological Service, on the period between January 1, 2006 and May 1, 2013 there have occurred 5,826 earthquakes which magnitude has been greater than 4.0 degrees on the Richter magnitude scale (25.54% of the total of earthquakes registered on the national territory), being the Pacific Plate and the Cocos Plate the most important ones. This document describes the development of an Artificial Neural Network (ANN) based on the radial topology which seeks to generate a prediction with an error margin lower than 20% which can inform about the probability of a future earthquake one of the main questions is: can artificial neural networks be applied in seismic forecasting? It can be argued that research has the potential to bring in the forecast seismic, more research is needed to consolidate data and help mitigate the impact caused by such events linked with society. Keywords--- Analysis, Mexico, Neural Artificial Networks, Seismicity.