A Novel Inversion Framework for Gravity and Magnetic Field Data: Geophysical Model Construction, Forward Modeling and Inversion

Abstract

Deep learning-based 3D inversion of gravity and magnetic field data can directly predict the spatial distribution and physical property parameters of subsurface source bodies from observed data, offering high resolution and efficiency. However, in practical applications, its generalization capability is constrained by two factors: the extent to which the training dataset approximates real geological conditions, and the network's capacity to extract data features. Previous model construction, forward modeling, and inversion methods are reviewed and compared. First, to tackle the problem that existing modeling methods cannot simulate rock bodies with gradational morphologies and physical properties, the continuous smoothness characteristics of Perlin noise are introduced for 3D geophysical model construction, enabling the simulation of realistic geometric shapes and physical properties of source bodies. Second, to address the dataset construction efficiency issue, a fast surrogate network is proposed, significantly accelerating batch forward modeling while maintaining precision. Third, a novel network integrating convolutional neural networks and Transformers is designed, combined with a dynamic noise-adding strategy, effectively enhancing the resolution of inversion results. In addition, to better quantify and evaluate inversion reliability, a new evaluation metric is proposed that characterizes both spatial and physical property consistency between the predicted model and the true model. Subsequently, a deep learning inversion framework is proposed by integrating the above methods, aiming to enhance the generalization ability and practicality of deep learning-based 3D inversion of gravity and magnetic data. Tests on synthetic data demonstrate the effectiveness of the proposed methods. Finally, the generalization capability of the inversion method and the effectiveness of the evaluation metric are discussed, and the method is validated on real data from the San Nicolas deposit in Mexico, yielding a subsurface 3D density model that is consistent with available geological and borehole information, and confirming the effectiveness and feasibility of the method.