Physics-Guided Deep Learning 3-D Inversion Based on Magnetic Data

The 3-D inversion of magnetic data based on deep learning has achieved great success. This method relies on neural networks to extract features from a large amount of data and then generate structures, with high prediction accuracy and fast speed. However, this data-driven inversion method lacks a closed-form analytical expression and is likened to a “black box.” This feature makes it lack theoretical guidance and has poor interpretability. In addition, the training data often cannot fully cover all aspects of the target, so data-driven methods face the challenge of weak generalization ability in practical applications. Therefore, this letter proposes to integrate physical knowledge into the inversion method based on deep learning. The architecture and loss function of the deep learning model are designed based on the forward modeling of the magnetic field. This can not only improve the interpretability of the algorithm and provide a deeper understanding of the model but also help to make up for the lack of generalization capabilities of deep learning methods.