Reconstruction of geomagnetic data based on artificial neural network

In this work, we used the back-propagation neural network to reconstruct missing geomagnetic data based on data of adjacent observatories. The simulation results show that reconstructed data are close to original data. The correlation of their power spectral density is about 1. 0. The average residual between reconstructed data and original data is about 0. 11 nT in quiet days, and reaches 0. 23 nT in disturbed days. When we use this method to reconstruct the data of a short time period with intense magnetic disturbance, the residual reduces to 0. 2 nT from 0. 4 nT. Based on the comparison of time and frequency domains, we suggest that the back-propagation network is an effective tool for geomagnetic data reconstruction.