A geomagnetic variation prediction method based on chaotic VMD-LSTM neural network

In view of the nonstationarity and nonlinearity of geomagnetic variation field and the difficulty of physical model prediction, an improved LSTM （long short-term memory） neural network prediction method is proposed and verified by tests. Firstly, the variational mode decomposition （VMD） method is used to denoise the geomagnetic data, and then the chaos theory is introduced to optimize the sample set according to the chaotic characteristics of geomagnetic variation. Finally, the LSTM network is used to predict geomagnetic variation. The results show that the prediction of the optimized method is relatively stable, mean absolute error is less than 2 nT, correlative coefficient R² is larger than 0.8, the predicted value is well consistent with the measured value, and the effective prediction time can reach 2.5 days, and it has good universality in Chinese Mainland.