Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

• Published in: Annals of Geophysics
• Main Authors: Popova, Irina, Rozhnoi, Alexander, Solovieva, Maria, Levin, Boris, Hayakawa, Masashi, Hobara, Yasuhide, Biagi, Pier Francesco, Schwingenschuh, Konrad
• Format: Article in Journal/Newspaper
• Language: English
• Published: Istituto Nazionale di Geofisica e Vulcanologia, INGV 2013
• Subjects: VLF/LF signal propagation; Neural network; Earthquakes; Wave propagation; Seismic risk; Petropavlovsk; Petropavlovsk-Kamchatsky; Kamchatka
• Online Access: https://www.annalsofgeophysics.eu/index.php/annals/article/view/6224; https://doi.org/10.4401/ag-6224

Very-low-frequency/ low-frequency (VLF/LF) sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself). To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007), and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event) and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.