Large Earthquake Occurrence Estimation Based on Radial Basis Function Neural Networks

This paper presents a novel scheme for the estimation of large earthquake event occurrence based on radial basis function (RBF) neural network (NN) models. The input vector to the network is composed of different seismicity rates between main events, which are easy to calculate in a reliable manner. Training of the NNs is performed using the powerful fuzzy means training algorithm, which, in this case, is modified to incorporate a leave-one-out training procedure. This helps the algorithm to account for the limited number of training data, which is a common problem when trying to model earthquakes with data-driven techniques. Additionally, the proposed training algorithm is combined with the Reasenberg clustering technique, which is used to remove aftershock events from the catalog prior to processing the data with the NN. In order to evaluate the performance of the resulting framework, the method is applied on the California earthquake catalog. The results show that the produced RBF model can successfully estimate interevent times between significant seismic events, thus resulting to a predictive tool for earthquake occurrence. A comparison with a different NN architecture, namely, multilayer perceptron networks, highlights the superiority of the proposed approach.