A Novel Method for Eliminating the Strip-Shaped Interferences in Aeromagnetic Anomaly Based on Convolutional Neural Network

Abstract

The strip-shaped interferences in aeromagnetic data pose great adverse effects on data interpretation. In literature, a number of methods have been proposed to deal with this problem. However, existing methods still have some limitations. Inspired by the ability of deep learning techniques to extract features from data, this study presents a novel convolutional neural network (CNN)-based method to eliminate the strip-shaped interferences in aeromagnetic data. The proposed method uses the U-Net structure to establish the whole network. The use of up–down sampling and skip connection enables the network to extract multiscale strip-shaped interferences with complex distribution and morphological characteristics. The basic theoretical formulas of the network and its architecture are presented, along with the construction method of the training dataset. Afterward, the presented method is tested on several synthetic examples and real aeromagnetic data collected in Jining, Inner Mongolia, and is compared with the conventional directional cosine filter to display its advantage in accuracy. The results demonstrate that the presented method can eliminate the strip-shaped interferences in aeromagnetic data effectively while preserving the features due to the real geological sources without any subjective parameters.