Magnetic data have been widely used for understanding basin structures, mineral deposit systems, formation history of various geological systems, and many others. Proper interpretation of magnetic data requires an accurate knowledge of total magnetization directions of the source bodies in the study area. Existing approaches for estimating magnetization directions involve either unstable data processing steps such as reduction-to-pole, component conversions in wavenumber domain, or computationally intensive processes such as 3D inversions. In this study, we developed a new method of automatically predicting the magnetization direction of a magnetic source body using Convolutional Neural Networks (CNN). CNNs have achieved great success in many other applications such as computer vision and seismic image interpretation, but have not been used to extract parameters from magnetic data. We simulated many magnetic data maps with different magnetization directions from a synthetic source body, all subject to the same background field. Two CNNs were trained separately, one for predicting the inclination and the other for predicting declination. We systematically trained and compared 13 different CNN architectures and selected one based on accuracy statistics. In addition, we investigated the effect of having different parameters such as magnetization magnitude and source body shape and location, on the performance of our predictive models. We also tested the method with field data from Black Hill norite, Australia, and Yeshan region, China, for which prior research results are available for comparison. Our study shows that machine learning provides an effective means of automatically predicting magnetization directions based on magnetic data maps. The files provided contain the training data maps used in the manuscript, as well as the Python scripts and Jupyter Notebooks to generate the training set and predict the magnetization inclination and declination.

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