Three-dimensional joint inversion of gravity and gravity gradient data based on data space and sparse constraints

With the increasing maturity of gravity and gravity gradient measurement technology, inversion technology based on gravity and gravity gradient data has been widely concerned. Aiming at the difficult problem of multiple inversions, low calculation efficiency, and large memory consumption,this paper has carried out a joint inversion study of 3D gravity and gravity gradient data. This method combines gravity and gravity gradient data, adds the L0 norm regulation term to the objection function, and uses an improved conjugate gradient algorithm to solve the inversion optimization problem in the data space. Meanwhile, this paper abandons the depth weighting function that relies on prior information and introduces the adaptive model integral sensitivity matrix to overcome the skin effect problem caused by the attenuation of the kernel function of gravity and gravity gradient data with depth. To improve the efficiency of inversion calculation, a fast forward calculation method of gravity and gravity gradient based on regular meshing is derived. Simulation experiments show that the improved conjugate gradient method can reduce the number of iterations of the inversion and improve the convergences speed of the inversion. The adaptive model integral sensitivity matrix, which can effectively solve the skin effect and can improve the longitudinal resolution of the inversion. The combination of data space and improved conjugate gradient method can better reduce the dimension of the inversion solution equation, avoid storing the sensitivity matrix, and effectively reduce the inversion calculation time and memory consumption. Field examples show that the proposed method can quickly obtain the underground density distribution model under ordinary computer, which shows strong stability and applicability.