Dip Filter and Random Noise Suppression for GPR B-Scan Data Based on a Hybrid Method in f - x Domain

Ground-penetrating radar (GPR) is a close-range remote-sensing tool applied in a great many near-surface projects for engineering or environmental purposes. In GPR B-scans, there may exist a variety of reflections and diffractions that corresponds to different structures and targets in the subsurface media, and the noise is always embedded. To assist in the interpretation, GPR B-scans can be generally divided into two parts according to the dip attribute of the reflections, where the sub-horizontal layers and dipping structures are properly separated. In this work, we extend the <i>f - x</i> empirical mode decomposition (<i>f - x</i> EMD) to form a semi-adaptive dip filter for GPR data. In <i>f - x</i> domain, each frequency slice is decomposed by EMD and reconstructed to form a dipping profile and a horizontal profile respectively, where the reflections at different dips are separated adaptively. Then the noises mixed in the dipping profile are further separated by rank-deduction methods in <i>f - x</i> domain. The above two-step scheme constitutes the hybrid scheme, which can separate the dipping structures, sub-horizontal layers, and most of the random noise in GPR B-scans. We briefly review the basics of the <i>f - x</i> EMD, and then introduce the derived hybrid scheme in <i>f - x</i> domain. The proposed method is tested by the synthetic data, the forward simulation data, and the field data, respectively.

Abstract licence: CC BY

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