The seismic electromagnet signal recognition using convolutional neural network

Seismic waveform data acquired by various seismic monitoring instruments are the base of understanding the mechanism of seismic research and disaster reduction. How to extract data and eliminate noise from a mass of valuable seismic data has become a hot issue in seismic research. A method based on convolutional neural network is proposed to solve the problem of seismic electromagnetic signal recognition, which employed a set of larger than M s 3.6 seismic event data recorded by electromagnetic instrument in Sichuan-Yunnan region. The electromagnetic signal is first visualized into a two-dimensional picture using short-time Fourier transform (STFT), so the problem of electromagnetic signal recognition is transformed into the object detection problem in the field of image recognition. A convolutional neural network method was used to train and test dataset from 1117 earthquake events. The training and detection accuracy rate of the dataset of 164 stations has reached 90%. The experiments show that this algorithm can deal with the problem of electromagnetic signal recognition and classify small sample size waveform data effectively.