Neural network technique for identifying prognostic anomalies from low-frequency electromagnetic signals in the Kuril–Kamchatka region

In this paper, we suggest a technique for forecasting seismic events based on the very low and low frequency (VLF and LF) signals in the 10 to 50 Hz band using the neural network approach, specifically, the error back-propagation method (EBPM). In this method, the solution of the problem has two main stages: training and recognition (forecasting). The training set is constructed from the combined data, including the amplitudes and phases of the VLF/LF signals measured in the monitoring of the Kuril-Kamchatka region and the corresponding parameters of regional seismicity. Training the neural network establishes the internal relationship between the characteristic changes in the VLF/LF signals a few days before a seismic event and the corresponding level of seismicity. The trained neural network is then applied in a prognostic mode for automated detection of the anomalous changes in the signal which are associated with seismic activity exceeding the assumed threshold level. By the example of several time intervals in 2004, 2005, 2006, and 2007, we demonstrate the efficiency of the neural network approach in the short-term forecasting of earthquakes with magnitudes starting from M ≥ 5.5 from the nighttime variations in the amplitudes and phases of the LF signals on one radio path. We also discuss the results of the simultaneous analysis of the VLF/LF data measured on two partially overlapping paths aimed at revealing the correlations between the nighttime variations in the amplitude of the signal and seismic activity.