Fast 3D inversion of gravity data using solution space priorconditioned lanczos bidiagonalization

• The required time and memory for gravity data inversion can be decreased by priorconditioned lanczos bidiagonalization.
• The orthonormal basis vectors of the discrete cosine transform can be applied for solution space priorconditioning.
• We performed a number of inversions of synthetic and real cases, the proposed method works for fast inversion of the data.

Inversion of gravity data is one of the most important steps in the quantitative interpretation of practical data. Inversion is a mathematical technique that automatically constructs a subsurface geophysical model from measured data, incorporating some priori information. Inversion of gravity data is time consuming because of increase in data and model parameters. Some efforts have been made to deal with this problem, one of them is using fast algorithms for solving system of equations in inverse problem. Lanczos bidiagonalization method is a fast algorithm that works based on Krylov subspace iterations and projection method, but cannot always provide a good basis for a projection method. So in this study, we combined the Krylov method with a regularization method applied to the low-dimensional projected problem. To achieve the goal, the orthonormal basis vectors of the discrete cosine transform (DCT) were used to build the low-dimensional subspace. The forward operator matrix replaced with a matrix of lower dimension, thus, the required memory and running time of the inverse modeling is decreased by using the proposed algorithm. It is shown that this algorithm can be appropriate to solve a Tikhonov cost function for inversion of gravity data. The proposed method has been applied on a noise-corrupted synthetic data and field gravity data (Mobrun gravity data) to demonstrate its reliability for three dimensional (3D) gravity inversion. The obtained results of 3D inversion both synthetic and field gravity data (Mobrun gravity data) indicate the proposed inversion algorithm could produce density models consistent with true structures.