A computationally efficient scheme for the inversion of large scale potential field data: Application to synthetic and real data

Three dimensional (3D) inversion of potential field data from large scale surveys attempts to recover density or magnetic susceptibility distribution in the subspace for geological interpretation. It is computationally challenging and is not feasible on desktop computers. We propose an integrated scheme to address this problem. We adopt adaptive sampling to compress the dataset, and the cross curve of the data compression ratio and correlation coefficient between the initial and sampled data is used to choose the damping factor for adaptive sampling. Then, the conventional inversion algorithm in model space is transformed to data space, using the identity relationship between different matrices, which greatly reduces the memory requirement. Finally, parallel computation is employed to accelerate calculation of the kernel function. We use the conjugate gradient method to minimize the objective function and a damping factor is introduced to stabilize the iterative process. A wide variety of constraint options are also considered, such as depth weighing, sparseness, and boundary limits. We design a synthetic magnetic model with three prismatic susceptibility causative bodies to demonstrate the effectiveness of the proposed scheme. Tests on synthetic data show that the proposed scheme provides significant reduction in memory and time consumption, and the inversion result is reliable. These advantages hold true for practical field magnetic data from the Hawsons mining area in Australia, verifying the effectiveness of the proposed scheme.