3D gravity fast inversion based on Krylov subspace methods

Mapping the density contrast through the 3D gravity inversion can help detect goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. The Krylov subspace method is commonly used for large linear problems due to its high computational efficiency and low storage requirement. In this study, two classical algorithms of Krylov subspace method, namely the generalized minimum residual method and the conjugate gradient method, are applied to 3D gravity inversion. On the basis of the recovered models of the deep mineral and the shallow L-shaped tunnel models, it was found that the generalized minimum residual method provided similar density contrast results to the conjugate gradient method. The obtained inversion results of density contrast corresponded well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D distribution of Fe content underground was obtained by inverting the measured gravity data from the Olympic Dam in Australia. The recovered results correspond well with the distribution of Fe content in the geological profile collected. The accuracy of inversion using the generalized minimum residual method was similar to that of the conjugate gradient method under the same conditions. However, the generalized minimum residual method had a faster convergence speed and increased inversion efficiency by similar to 90%, greatly reducing the inversion time and improves the inversion efficiency.