TY  - JOUR
A1  - Allroggen, Niklas
A1  - Tronicke, Jens
T1  - Attribute-based analysis of time-lapse ground-penetrating radar data
T2  - Geophysics
N2  - Analysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tested and evaluated our attributes using synthetic data of different complexity. Afterward, we applied them to a field data example, in which subsurface flow was induced by an artificial rainfall event. For all examples, we identified our structural similarity attribute to be a robust measure for highlighting time-lapse changes also in data with low signal-to-noise ratios. We determined that our new attribute-based workflow is a promising tool to analyze time-lapse GPR data, especially for imaging subsurface hydrological processes.
Y1  - 2016
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/45799
SN  - 0016-8033
SN  - 1942-2156
VL  - 81
SP  - H1
EP  - H8
PB  - Society of Exploration Geophysicists
CY  - Tulsa
ER  -