Research on Seismic Signal Identification and Magnitude Prediction Model Based on Sample Entropy and Machine Learning

Based on advanced artificial intelligence technology, this study explores for earthquake early warning and magnitude prediction. By analyzing seismic signals through CEEMDAN, constructing sample entropy feature vectors and applying various machine learning models, natural seismic events are successfully identified. In terms of magnitude prediction, combining LSTM and other models, we effectively extracted seismic wave features and realized accurate prediction. Through data analysis and model optimization, the association between features and magnitude is deeply explored, providing new ideas for earthquake research. The study also explores the connection between reservoir attributes and magnitude. This paper innovatively reveals the CEEMDAN sample entropy analysis method, which brings new ideas and methods to the field of seismology. These results are of great significance to the development of earthquake early warning technology and earthquake prevention measures and provide useful references for future related research.