Can Artificial Neural Networks be Applied in Seismic Predicition? Preliminary Analysis Applying Radial Topology. Case: Mexico
This research addresses seismic forecasting to potentially mitigate losses from earthquakes in Mexico, but it appears incremental as it applies a known neural network method to a specific dataset without demonstrating clear advancements.
The paper tackles the problem of predicting earthquakes in Mexico using an artificial neural network with radial topology, aiming for an error margin below 20% to forecast earthquake probability, but does not report specific results or numbers from the analysis.
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.