2015/10/09 by R. W. Jacob, Robert W. Jacob, T. M. Urban +1 · 1 citation
Earth and Planetary Sciences · Engineering · Mathematics · #Computer science #Cross-correlation #Data processing #Excavation #Geodesy #Geology #Geometry #Geophysical Methods and Applications #Geotechnical engineering #Ground-penetrating radar #Hyperbola #Mathematics #Midpoint #Offset (computer science) #Radar #SIGNAL (programming language) #Seismic Imaging and Inversion Techniques #Seismic Waves and Analysis #Spurious relationship #Statistics #Telecommunications
paper · pdf · doi:10.1111/arcm.12214
openalex publication_date 2015/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
The most crucial parameter to be determined in an archaeological ground-penetrating radar (GPR) survey is the velocity of the subsurface material. Precision velocity estimates comprise the basis for depth estimation, topographic correction and migration, and can therefore be the difference between spurious interpretations and/or efficient GPR-guided excavation with sound archaeological interpretation of the GPR results. Here, we examine the options available for determining the GPR velocity and for assessing the precision of velocity estimates from GPR data, using data collected at a small-scale iron-working site in Rhode Island, United States. In the case study, the initial velocity analysis of common-offset GPR profile data, using the popular method of hyperbola fitting, produced some unexpectedly high subsurface signal velocity estimates, while analysis of common midpoint (CMP) GPR data yielded a more reasonable subsurface signal velocity estimate. Several reflection analysis procedures for CMP data, including hand and automated signal picking using cross-correlation and semblance analysis, are used and discussed here in terms of efficiency of processing and yielded results. The case study demonstrates that CMP data may offer more accurate and precise velocity estimates than hyperbola fitting under certain field conditions, and that semblance analysis, though faster than hand-picking or cross-correlation, offers less precision.