A Sparse 2-D Magnetic Inversion Method for Subsurface Target Classification

Abstract

Magnetic inversion plays an important role in target classification and recognition. However, traditional magnetic susceptibility inversion methods often fall into local minima, and require high storage space and computing power. This letter proposes a novel 2-D magnetic inversion method for compact ferrous targets, the 3-D object is projected onto a 2-D inversion plane and a linear inversion model based on the modulus of magnetic moment on a 2-D horizontal slice is created. The compact anomaly target is composed of multiple nonzero uniformly connected grids in spatial domain, so the target is sparse in the gradient domain, because the nonzero elements on the inversion plane are only located at the boundary of the target. Using the sparseness, total variation compressed sensing (TV-CS) framework is employed to solve the 2-D magnetic inversion problem, achieving rapid classification and identification of hidden targets. In addition, numerical simulations have been performed, and experimental results indicate that the proposed algorithm is able to accurately delineate targets of different shapes and distinguish between solid and hollow targets. For a compact 3-D target, the 2-D magnetic inversion time is 0.096 s with a spatial resolution of 0.5 m, and 2.36 s with a spatial resolution of 0.1 m.