Fast 3D subsurface imaging with stepped-frequency GPR
This paper investigates an algorithm for forming 3D images of the subsurface using stepped-frequency GPR data. The algorithm is specifically designed for a handheld GPR and therefore accounts for the irregular sampling pattern in the data and the spatially-variant air-ground interface by estimating an effective “ground-plane” and then registering the data to the plane. The algorithm efficiently solves the 4th-order polynomial for the Snell reflection points using a fully vectorized iterative scheme. The forward operator is implemented efficiently using an accelerated nonuniform FFT (Greengard and Lee, 2004); the adjoint operator is implemented efficiently using an interpolation step coupled with an upsampled FFT. The imaging is done as a linearized version of the full inverse problem, which is regularized using a sparsity constraint to reduce sidelobes and therefore improve image localization. Applying an appropriate sparsity constraint, the algorithm is able to eliminate most the surrounding clutter and sidelobes, while still rendering valuable image properties such as shape and size. The algorithm is applied to simulated data, controlled experimental data (made available by Dr. Waymond Scott, Georgia Institute of Technology), and government-provided data with irregular sampling and air-ground interface.
Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX
Masarik, M. P.,
Burns, J. W.,
Fast 3D subsurface imaging with stepped-frequency GPR.
Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX,
Retrieved from: https://digitalcommons.mtu.edu/mtri_p/238