Interpolation methods for recovering the sampling values of GRP data
Author(s) -
OKAY Merve Özkan SAMET
Publication year - 2017
Publication title -
communications faculty of sciences university of ankara series a2-a3 physical sciences and engineering
Language(s) - English
Resource type - Journals
ISSN - 1303-6009
DOI - 10.1501/commua1-2_0000000100
Subject(s) - ground penetrating radar , interpolation (computer graphics) , data collection , excavation , data mining , computer science , visualization , identification (biology) , sampling (signal processing) , completeness (order theory) , similarity (geometry) , missing data , remote sensing , radar , geology , artificial intelligence , computer vision , geotechnical engineering , statistics , machine learning , mathematics , image (mathematics) , telecommunications , mathematical analysis , botany , biology , filter (signal processing)
Ground Penetrating Radar (GPR) is widely used to acquire the data from near surface depth. The acquired GPR data allow the users to investigate and examine the underground structures (anomalies) easily, quickly and accurately without any excavation. In GPR studies, data collection parameters such as the profile interval and step size, which can be controlled by users, play an important role in the identification of underground structures. But search area properties such as uneven surface, the presence of archaeological and other obstacles cannot be controlled by users. The obtained accuracy depends on the completeness and resolution of acquired GPR data. Due to some research area properties the data acquired from the search area may become incomplete and inadequate. Before analyzing, visualization and interpretation of the underground structures, the incomplete GPR data should be recovered. In this paper, nonstandard interpolation method are proposed for completing the missing data. The proposed methods were implemented on the real GPR data acquired from the test area. The obtained results showed that the similarity of the produced data as quite closer to the original data.
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