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3D MAGNETIC INVERSION WITH SPARSENESS CONSTRAINTS BASED ON RESIDUAL NODE SUSCEPTIBILITY

Chunxiao Xiu, Xiaohong Meng, Sheng Zhang, Xingdong Zhang

Abstract

For generalized inversion, non L2-norm inversions have been widely used in geophysical inversion to obtain sharp and blocky models. However, problems such as the boundary of anomaly body obtained by inversion is not clear enough and the recovered residual susceptibility value is far less than or greater than the true susceptibility generally exist. In this paper a new 3D magnetic inversion approach based on residual node susceptibility is proposed. The residual node susceptibility model comes from residual block susceptibility model and it has special sparse properties for the underground structure, especially for blocky models. Cauchy norm is used to impose sparseness on model parameters. Depth weighting function essentially allows equal chance for blocks at different depths to be nonzero to avoid the пїЅskin effectпїЅ. Iterative Reweighted Least Square algorithm is employed to solve the inverse problems. Then the residual node susceptibility model need to be converted into residual block susceptibility model through the zeros boundary layer. Synthetic data test and real data proved the effectiveness of the proposed method.

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DOI resolver: doi.org/10.5593/sgem2017/13/s03.001

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