Earthquakes magnitude prediction using recurrent neural networks

Seismological research importance around the globe is very clear, therefore new tools and algorithms are needed in order to predict magnitude, time and geographic location, as well as found out relationships that allow us to understand better this phenomenon and thus be able to save countless human lives. However, given the highly random nature of the earthquakes and the complexity in obtaining an efficient mathematical model, until now the efforts are insufficient and new methods capable of contributing to this challenge are needed.

In this work a novel prediction method is proposed, which is based on the composition of a known system whose behavior is governed according to the measurements of more than two decades of seismic events and is modeled as a non-linear system using machine Learning, specifically a recurrent neural network, architecture based on long-short term memory (LSTM) cells.