Deep Learning-Based 3D Gravity Inversion with Well-Logging Prior Information

As a fundamental technique in geophysical exploration, gravity inversion plays a crucial role in geological structure interpretation and mineral resource assessment. Recent advances in deep learning, particularly the remarkable capabilities of convolutional neural networks (CNNs) in image recognition, object detection, and semantic segmentation, have provided innovative solutions to nonlinear inverse problems in geophysics. However, conventional data-driven approaches often yield physically unrealistic inversion results due to the inherent non-uniqueness of solutions. This study proposes a novel 3D deep-learning gravity inversion framework incorporating well-logging prior constraints, which establishes a density-position relationship-based constraint mechanism through synergistic integration of surface gravity anomaly data and downhole petrophysical parameters. We develop a new neural network architecture that incorporates well-logging data as hard constraints for gravity inversion while preserving both density characteristics and spatial information of subsurface formations. Significantly, we implement the Convolutional Block Attention Module (CBAM) during network optimization, enabling selective enhancement of lithological features during backpropagation. This attention-guided mechanism achieves robust coupling between potential field anomalies and petrophysical parameters from well logs, substantially improving the spatial accuracy of 3D density reconstruction. Numerical experiments demonstrate that our method can accurately recover the density distribution of subsurface ore bodies. Comparative results show that the integration of well-logging information significantly enhances both solution reliability and structural consistency when compared to purely surface data-driven approaches. In practical application to field data from the San Nicolas sulfide deposit in Mexico, the proposed method outperforms conventional UNet-based approaches in terms of inversion performance.