Direct‐wave denoising of low‐frequency ground‐penetrating radar in open pits based on empirical curvelet transform

ABSTRACT Low‐frequency ground‐penetrating radar is known to be effective in the detection of geological disasters in open pits owing to its good detection depth, high resolution and portability. However, because of the lack of a shield layer and the poor anti‐interference capability, low‐frequency ground‐penetrating radar has certain limitations. A strong linear interference due to direct waves can result in a complex mining environment, hindering the detection of geological disasters in open pits. Conventional methods, such as wavelet transform and curvelet transform, are ineffective, as they cannot adaptively remove noise in accordance with signal characteristics. In this paper, considering the high apparent velocity and energy of direct‐wave noise in open‐pit ground‐penetrating radar data, an empirical curvelet transform method is proposed to suppress the interference signals. The empirical curvelet transform not only is multi‐scale and multi‐directional, but can also adaptively perform filtering based on the characteristics of the ground‐penetrating radar interference signals. This method is applied to the simulation and processing of measured ground‐penetrating radar data and is compared with the conventional curvelet transform. The results confirm the effectiveness of this method.