A seismic wave vibration nature recognition method based on machine learning, wherein the method relates to the technical field of machine learning. The implementation of the method is divided into four stages: seismic waveform processing, feature value extraction, model training, and model application, involving: calculating the distance between a seismic source and a station, screening an epicentral distance, reading three-component seismic waveform data of the screened epicentral distance, using a long time-window algorithm/short time-window algorithm (STA/LTA) and an AIC method to accurately find the location of a first movement of seismic waves, and further intercepting a seismic data length; respectively performing time domain analysis and frequency domain analysis on a seismic waveform; extracting three characteristic waveform complexities, a spectrum ratio, and waveform complexity/spectrum ratio as an input of an artificial neural network model; and training an artificial neural network model with two hidden layers used to identify a two-classification problem, and outputting the probability of a category to which the waveform belongs. The model trained by the method can accurately and efficiently determine a category to which a waveform belongs.
SEISMIC WAVE VIBRATION NATURE RECOGNITION METHOD BASED ON MACHINE LEARNING