2D resistivity inversion using conjugate gradients for a finite element discretization

Abstract:

In this work we present a DC 2D inversion algorithm using conjugate gradients relaxation to solve the maximum likelihood inverse equations. We apply, according to Zhang (1995), the maximum likelihood inverse theory developed by Tarantola and Valette (1982) to our 2D resistivity inversion. This algorithm was chosen to this research because it doesn’t need to calculate the field’s derivatives. Since conjugate gradient techniques only need the results of the sensitivity matrix à or its transpose Ã^T multiplying a vector, the actual computation of the sensitivity matrix are not performed, according to the methodology described in Zhang (1995). In Zhang (1995), the terms Ãx and Ã^Ty, are dependent of the stiffness matrix K and its partial derivative ∂K⁄∂ρ. The inversion methodology described in Zhang (1995) is for the case of 3D electrical resistivity by finite differences discretization. So it was necessary to make a series of adjustments to obtain a satisfactory result for 2D electrical inversion using finite element method.

The difference between the modeling of 3D resistivity with finite difference and the 2D finite element method are in the integration variable, used in the 2D case. In the 2D case the electrical potential are initially calculated in the transformed domain, including the stiffness matrix, and only in the end is transformed in Cartesian domain. In the case of 3D, described by Zhang (1995) this is done differently, the calculation is done directly in the Cartesian domain. In the literature was not found any work describing how to deal with this problem. Because the calculations of Ãx and Ã^Ty must be done without having the real stiffness matrix, the adaptation consist in calculate the stiffness matrix and its partial derivative using a set of integration variables. We transform those matrix in the same form has in the potential case, but with different sets of variables. The results will be presented and are very promising.