Extraction of Remanent Magnetization Intensity and Direction Based on ResU-Net

Abstract

The presence of remanent magnetization introduces uncertainties in the processing and interpretation of magnetic data. In the literature, a variety of methods have been proposed to extract the intensity and direction of remanent magnetization. However, the existing methods still have some limitations, such as biases in results due to the use of inaccurate prior information and the complex computational process of extracting remanent magnetization information, especially from superimposed anomalies by multiple field sources. In this study, we develop an effective method to extract the intensity and direction of the remanent magnetization based on deep learning. We first use an improved U-Net as the backbone network to obtain the feature of spatial location and remanent magnetization parameters of anomalies and fuse the extracted multiscale feature information. At the same time, residual connections are added between the convolution layers to alleviate the loss of information and reduce gradient disappearance. The network, through continuous training, can directly learn the nonlinear mapping relationship between anomalies and the remanent magnetization intensity and direction, without the need for a prior information and complex calculations. Subsequently, we test the proposed method on synthetic examples and field data example in Yeshan region. All the outcomes demonstrate the capability in accurately extracting intensity and direction of remanent magnetization.