Ground-Penetrating Radar Mapping Using Multiple Processing and Interpretation Methods
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2016
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Abstract
Ground-penetrating radar processing and interpretation methods have been developed over time that usually follow a certain standard pathway, which leads from obtaining the raw reflection data to the production of amplitude slice-maps for three-dimensional visualization. In this standard series of analysis steps a great deal of important information contained in the raw data can potentially be lost or ignored, and without careful consideration, data filtering and re-analysis, information about important buried features can sometimes be unobserved. A typical ground-penetrating radar (GPR) dataset should, instead, be processed, re-evaluated, re-processed and then new images made from new sets of data as a way to enhance the visualization of radar reflections of interest. This should only be done in an intuitive way, once a preliminary series of images are produced using standard processing steps. An example from data collected in an agricultural field in France illustrate how obvious buried features are readily discovered and interpreted using standard processing steps, but additional frequency filtering, migration and then re-processing of certain portions of the data produced images of a subtle Roman villa foundation that might have otherwise gone undiscovered. In sand dunes in coastal Brazil, geological complexity obscured the reflections from otherwise hidden anthropogenic strata, and only an analysis of multiple profiles using different scales and processing allowed this small buried feature to be visible. Foundations of buildings in a Roman city in England could be easily discovered using standard processing methods, but a more detailed analysis of reflection profiles after re-processing and a comparison of GPR images with magnetic gradiometry maps provided information that allowed for the functions of come buried buildings and also an analysis of the city’s destruction by fire.
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conyers2016groundpenetratingremote
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| Authors | Conyers, Lawrence B.; |
| Journal | remote sensing |
| Year | 2016 |
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