Abstract
Magnetic compensation is a necessary step in aeromagnetic data processing. The aeromagnetic compensation model is a linear regression model, but the model has multiple collinearity problems, which will reduce the performance of the compensation model. In view of this problem, we propose a deep autoencoder (DAE) aeromagnetic compensation algorithm. The DAE network extracts the features of the data by learning the compressed representation of the coefficient matrix, thereby weakening the correlation between the coefficient matrix variables. The feature obtained after dimension reduction is used for compensation calculation. The DAE algorithm is verified by Unmanned Aerial Vehicles, and the results show that the compensation quality of the DAE is better than the least squares algorithm.
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