Title

Applicability of Combined GPR and Gradiometer to Detect Buried Shallow Targets for Near-Surface Investigations

Document Type

Presentation

Publication Date

9-18-2019

Keywords

ground-penetrating radar (GPR), gradiometry, archaeology, engineering, environmental

Digital Object Identifier (DOI)

https://doi.org/10.1190/segam2019-3215352.1

Abstract

Applications of geophysical tools have always been an integral practice in near-surface characterization. We present an effective combination of ground penetrating radar (GPR) and magnetic gradiometry to detect buried objects within ~1 m of the surface. These objects are comprised of both metallic and non-metallic materials, representing a range of different sizes and shapes. To detect these features, we conducted 3D GPR and gradiometer surveys over an area of 40 m-by-50 m with 2m and 1 m grid spacing respectively. Overall, the datasets represent high signal-to-noise (S/N) ratio, although the gradiometer data were relatively noisier than the GPR at the southwestern part of the survey around the fence. Results show that the models derived from GPR and gradiometer were able to detect most of the features independently. However, when both methods are combined, we were able to identify all the targets, especially the coil of copper wire (target no. 14), which showed no indication in the GPR survey but was readily detectable with the gradiometer. With GPR, all the detectable objects represent an increase in dielectric constant with respect to the background. On the other hand, magnetic field gradient measured by the gradiometer represents both positive and negative magnetic field strengths. This is probably due to the metallic vs non-metallic nature of the objects with respect to the host material in the area. The metal well caps located at the surface were detected in both the surveys, which essentially were used as means to validate the models.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

SEG Technical Program Expanded Abstracts 2019, p. 2878-2882

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