An Aeromagnetic Compensation Algorithm Based on a Deep Autoencoder

Abstract

Magnetic compensation is a necessary step in the aeromagnetic data processing. While the aeromagnetic compensation model is a linear regression model, the multicollinearity of the variables in the model reduces the accuracy of the compensation model. To solve this problem, we propose a deep autoencoder (DAE) aeromagnetic compensation algorithm. The DAE searches the direction of maximum change in the data by using the gradient descent backpropagation algorithm. The special structure of the encoder can compress the representation of the coefficient matrix and extract data features, thereby weakening the correlation between the coefficient matrix variables. The features obtained after dimension reduction are used in the compensation calculation. We validate the DAE algorithm by applying it to data collected by an unmanned aerial vehicle and demonstrate that the DAE magnetic compensation results are better than those of the principal component analysis algorithm.