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

TL;DR

This paper explores the application of a radial topology-based artificial neural network to seismic prediction in Mexico, aiming to forecast earthquakes with an error margin below 20%, highlighting potential for seismic risk mitigation.

Contribution

It introduces a novel ANN model with radial topology for seismic prediction and evaluates its effectiveness in forecasting earthquakes in Mexico.

Findings

Achieved prediction error margin below 20%

Demonstrated potential of neural networks in seismic forecasting

Provided preliminary results supporting further research

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.