Author(s)

Vitor H. A. Dias¹, Andrés R. R. Papa^1,2

¹Geophysics Department, Observatório Nacional, Rio de Janeiro, Brazil.
²Physics Institute, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.

During the last few decades, many statistical physicists have devoted re-search efforts to the study of the problem of earthquakes. The purpose of this work is to apply methods of Statistical Physics and network systems based on “neurons” in the study of seismological events. Data from the Advanced National Seismic System (ANSS) of Southern California were used to verify the relationship between time differences between consecutive seismic events with magnitudes greater than 3.0, 3.5, 4.0 and 4.5 through the modeling of neural networks. The problem we are analyzing is time differences between seismological events and how these data can be adopted as a time series with non linear characteristic. We are therefore using the multilayer perceptron neural network system with a backpropagation learning algorithm, because its characteristics allow for the analysis of non-linear data in order to obtain statistical results regarding the probabilistic forecast of tremor occurrence.

Neurons, Multi-Layer Perceptron, Backpropagation, Prediction

Dias, V. and Papa, A. (2018) Application of Neural Networks in Probabilistic Forecasting of Earthquakes in the Southern California Region. International Journal of Geosciences, 9, 397-413. doi: 10.4236/ijg.2018.96025.