High-precision magnetization vector inversion: application to magnetic data in the presence of significant remanent magnetization

Abstract

Abstract Magnetization vector inversion is essential for obtaining magnetization vector information from subsurface rocks. To obtain focused inversion results that better match the true magnetization distributions, sparse constraints are considered to constrain the objective function. A compact magnetization vector inversion method is proposed that can provide accurate inversion results for magnetic data with significant remanent magnetization. Considering the sparse constraint and the correlation between the three magnetization components with different directions, the L1-norm is modified and introduced into the inversion algorithm to obtain compact results. Furthermore, to reduce the computational cost, a randomized singular value decomposition is used to replace the traditional singular value decomposition and iteratively minimize the proposed objective function. Two synthetic models with different magnetization directions are developed to verify the performance of the proposed method. The results of magnetization vectors obtained by the proposed method are focused and accurate. Finally, the proposed method is applied to igneous rocks with strong remanent magnetization in the Haba River area of northwestern China. The distributions, directions of total magnetization and remanent magnetization of the medium-base igneous rocks are revealed by the sparse magnetization vector inversion method, which provides a wealth of information about the concealed deposits in the area.