Single Layer Recurrent Neural Network for detection of swarm-like earthquakes in W-Bohemia/Vogtland—the method

In this paper, we present a new method of local event detection of swarm-like earthquakes based on neural networks. The proposed algorithm uses unique neural network architecture. It combines features used in other neural network concepts such as the Real Time Recurrent Network and Nonlinear Autoregressive Neural Network to achieve good performance of detection. We use the recurrence combined with various delays applied to recurrent inputs so the network remembers history of many samples. This method has been tested on data from a local seismic network in West Bohemia with promising results. We found that phases not picked in training data diminish the detection capability of the neural network and proper preparation of training data is therefore fundamental. To train the network we define a parameter called the learning importance weight of events and show that it affects the number of acceptable solutions achieved by many trials of the Back Propagation Through Time algorithm. We also compare the individual training of stations with training all of them simultaneously, and we conclude that results of joint training are better for some stations than training only one station.