Double-Sided Sliding-Paraboloid (DSSP): A new tool for preprocessing GPR data

Background noise in Ground Penetrating Radar (GPR) data is a nagging problem that degrades the quality of GPR images and increases their ambiguity. There are several methods adopting different strategies to remove background noise. In this study, we present the Double-Sided Sliding-Paraboloid (DSSP) as a new background removal technique. Experiments conducted on field GPR data show that the proposed DSSP technique has several advantages over existing background removal techniques. DSSP removes background noise more efficiently while preserving first arrivals and other strong horizontal reflections. Moreover, DSSP introduces no artifacts to GPR data and corrects data for DC-shift and wow noise. Display Omitted We propose a novel method for background noise removal in GPR data.The proposed method, named DSSP, is an extension of the rolling ball algorithm.DSSP method has several advantages in removing the background noise.Field GPR data are used for evaluation and indicate image quality improvement.

[1]  Mohamed Rashed,et al.  Rolling ball algorithm as a multitask filter for terrain conductivity measurements , 2016 .

[2]  Yih Jeng,et al.  Application of Multiresolution Analysis in Removing Ground-penetrating Radar Noise , 2009 .

[3]  M. Szymczyk,et al.  Preprocessing of GPR data , 2013 .

[4]  Aurélien Rizk,et al.  Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh , 2014, Nature Protocols.

[5]  Essam A. Rashed,et al.  GPR background removal using a directional total variation minimisation approach , 2015 .

[6]  T. Quarta,et al.  Improvement in GPR coherent noise attenuation using τ-p and wavelet transforms , 2004 .

[7]  Mohamed Rashed,et al.  Application of alternative seismic-stacking techniques to ringing noise removal from GPR data , 2013 .

[8]  T. Ulrych,et al.  Singular value decomposition and wavy reflections in ground-penetrating radar images of base surge deposits , 2001 .

[9]  Michael T. Bowers,et al.  A new algorithm to characterise the degree of concaveness of a molecular surface relevant in ion mobility spectrometry , 2015 .

[10]  D. Nobes.  Geophysical surveys of burial sites: A case study of the Oaro urupa , 1999 .

[11]  Mehmet Sezgin,et al.  Simultaneous buried object detection and imaging technique utilizing fuzzy weighted background calculation and target energy moments on ground penetrating radar data , 2011, EURASIP J. Adv. Signal Process..

[12]  Erich D. Guy,et al.  Pitfalls in GPR data interpretation: Differentiating stratigraphy and buried objects from periodic antenna and target effects , 2000 .

[13]  Seong-Jun Cho,et al.  Removal of ringing noise in GPR data by signal processing , 2007 .

[14]  Gholamreza Akbarizadeh,et al.  Detection of Lung Nodules in CT Scans Based on Unsupervised Feature Learning and Fuzzy Inference , 2016 .

[15]  Stanley R. Sternberg,et al.  Biomedical Image Processing , 1983, Computer.

[16]  Rosemary Knight,et al.  Processing Ground Penetrating Radar Data To Improve Resolution Of Near-Surface Targets , 1993 .

[17]  Roger A. Young,et al.  Revealing stratigraphy in ground-penetrating radar data using domain filtering , 1999 .

[18]  Alan P. Byrnes,et al.  Improving ground-penetrating radar data in sedimentary rocks using , 2003 .

[19]  Mohamed Rashed,et al.  Background matrix subtraction (BMS): A novel background removal algorithm for GPR data , 2014 .

[20]  Seong-Jun Cho,et al.  Effective 3D GPR survey and its application to the exploration of old remains , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..