Anomalous signal detection using multi-layer neural network for electromagnetic wave radiation

It is well known that the electromagnetic waves that radiate from the Earth's crust are useful for predicting earthquakes. We observe electromagnetic waves in the extremely low frequency (ELF) band of 223 Hz. These observed signals contain several undesired signals due to fluctuations in the magnetosphere or the ionized layer, lightning radiation from the tropics, and so on. This paper proposes a multi-layer neural network using compression data for precursor signal detection. Input data are reduced by the wavelet transform. Moreover, we discuss an implementation of the hidden layer. It is shown that the proposed neural network is useful for precursor signal detection.