Gated Recurrent Units Based Recurrent Neural Network for Forecasting the Characteristics of the Next Earthquake
Jesús González, Wen Yu, Luciano Telesca
Abstract
We are currently far from being able to make an accurate and timely prediction of earthquakes, that can tell us even roughly Where will it occur? When will it occur? What will its magnitude be? And at what depth will it occur? However, this information would be incredibly valuable to avoid loss of lives, damage to constructions, and a great economic loss. In this paper we propose a using recurrent neural networks (RNN) which have proven to be very efficient in time series analysis and based on the Gated recurrent unit (GRU) cell which is simple but powerful. This architecture achieves to solve these questions seconds before the earthquake and is applied to the seismicity of Italy from 1995 to 2018, considering the events with magnitude larger or equal to 1.5 and with a depth smaller than 60 km. Although a lot of work still needs to be done so that it can be applied in the real world as a prevention method, our results indicate that we are on the right track.