Joint Inversion of 3-D Gravity and Gradient Tensor Based on Adaptive Lp-Norm and Kernel-Fitted Depth Weighting

3D gravity inversion can reconstruct subsurface density distributions from gravity observation data and is widely used in geological structure research and resource exploration. However, traditional methods often face challenges such as strong non-uniqueness and low depth resolution when inverting 3D models from 2D observation data. Currently, the issue of nonuniqueness is excessively pronounced, causing significant difficulties in accurately resolving complex subsurface structures. To address this, this study proposes a novel joint inversion method of gravity and gradient tensor data that integrates adaptive Lp norm with an improved depth weighted strategy. By calculating the first-order derivative magnitudes of model parameters in the x, y, and z directions and substituting them into the p function, dynamic optimization of the norm selection is achieved. Simultaneously, nonlinear least squares are employed to fit the gravity and gravity gradient kernel functions using a depth weighted function, thereby optimizing the parameters within the depth weighted function. This method significantly enhances the inversion stability and the characterization accuracy of geological structure, providing an efficient and robust solution for 3D inversion of complex geological bodies. Synthetic model tests demonstrate that this method can more accurately characterize the location, size, and density amplitude of anomalous bodies compared with traditional inversion methods. Further application of this method to actual data from the Vinton Salt Dome in Louisiana, USA, yields inversion results that are similar to existing studies, validating the effectiveness of the method.