Magnetic data have been widely used for understanding basin structures, mineral deposit systems, the formation history of various geological systems, and many others. Proper interpretation of magnetic data requires accurate knowledge of total magnetization directions of the source bodies in an area of study. Existing approaches for estimating magnetization directions involve either unstable data processing steps or computationally intensive processes such as 3-D inversions. In this study, we developed a new method of automatically predicting the magnetization direction of a magnetic source body using convolutional neural networks (CNNs). 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 determined the optimal CNN architectures for predicting inclinations and declinations by systematically comparing 13 different CNN architectures. In addition, we investigated the effect of having different parameters such as magnetization magnitude, source body shape, location, and depth on the performance of our predictive models. We also tested the method using 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.

No abstract

No abstract