Sparsity Imaging for Potential Field Data Based on Orthogonal Matching Pursuit

Traditional approaches inverting for potential field data are based on observation data and model parameters constrains of l2-norm. However, the recovered physical properties distributions are prone to be fuzzy. To improve the imaging resolution, we present a sparsity imaging method of orthogonal matching pursuit (OMP) optimizing l0-norm model parameters constrains. Because the potential field data can be linearly combined by column vectors of sensitive matrix, we employ the column vector of sensitive matrix to be an atom and the whole sensitive matrix constitutes a over-complete atom dictionary of OMP. Simultaneously, we select the most correlative column vector with observed gravity and magnetic anomalies as the optimal atom to match. The OMP can rapidly invert for the sparse distributions of physical properties. The test of twodimensional (2D) synthetic data demonstrates that the method can recover the sharply physical properties boundaries and prevent them from concentrating near the surface.