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Location of Archaeological Structures using GPR Method: Three‐dimensional Data Acquisition and Radar Signal Processing
Author(s) -
MALAGODI S.,
ORLANDO L.,
PIRO S.,
ROSSO F.
Publication year - 1996
Publication title -
archaeological prospection
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.785
H-Index - 38
eISSN - 1099-0763
pISSN - 1075-2196
DOI - 10.1002/(sici)1099-0763(199603)3:1<13::aid-arp37>3.0.co;2-4
Subject(s) - ground penetrating radar , radar , geology , remote sensing , signal (programming language) , data acquisition , representation (politics) , data processing , computer science , telecommunications , politics , political science , law , programming language , operating system
Generally, in order to detect shallow archaeological features, such as tombs, cavities, walls, etc., ground penetrating radar (GPR) data are acquired along parallel profiles. In some cases, the data collected using the GPR method are difficult to interpret owing to the presence of low signal‐to‐noise (S/N) ratio. These signals can be generated by several factors that significantly influence the radar profiles. To enhance the interpretation of radar sections, three‐dimensional data acquisition, radar signal processing and time‐slice representation are used. The archaeological investigated as a test site was the Sabine Necropolis (700–300 BC) at Colle del Forno (Montelibretti, Roma), believed to contain unexplored underground dromos chamber tombs . The measurements were carried out along parallel profiles in a test area, using Sir System 10 (GSSI) equipped with different antennas operating at 100, 300 and 500 MHz. The spatial interval used during the survey was 20 cm. To enhance the S/N ratio, a band‐pass filter and subtraction of an average trace on the field data has been applied; furthermore, the two‐dimensional migration technique on all profiles collected was used in order to move diffraction effects. A time‐slice representation technique was adopted to obtain a planimetric correlation between anomalous bodies at different depths. The results indicate that the three‐dimensional data acquisition, processing and the time slice representation can help determine the location, depth and shapes of buried features.