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Kansas Geological Survey, Current Research in Earth Sciences, Bulletin 247, part 1
Modeling Dielectric-constant Values of Geologic Materials: An Aid to Ground-penetrating Radar Data Collection and Interpretation--page 9 of 13

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Discussion of Model Results

Comparison of the TP-modeled bulk dielectric constants with those measured on sandstone and limestone indicates that the TP-modeled values are reasonably well correlated with measured values. Both theoretical modeling (figs. 1 and 2) and measured data illustrate the significant influence of water saturation and porosity and the lesser influence of mineralogy on bulk dielectric constant and consequently on GPR response, including two-way travel time, reflection coefficient amplitude across interfaces of different materials, and vertical and horizontal imaging resolution.

The large increase in two-way travel-time (TWT) values with increasing water saturation (figs. 6, 7, and 8) indicates that care must be taken when interpreting GPR data taken at the same site if water-saturation conditions change between surveys or differ across the site due to proximity to a water recharge or discharge area. For the sandstone scenarios shown in figs. 6, 7, and 8, TWT increased rapidly with increasing saturation (fig. 11). Large travel-time changes induced by low water saturation differences may result in enough change in reflection-peak or trough-arrival times to cause mis-ties when interpreting data sets collected under different water saturation conditions. Similarly, it may result in misinterpretation of apparent bed dip if constant lateral saturations are assumed but are not present at a site.

Fig. 11. Calculated reflection coefficients for dry and water-saturated quartzose (epsilon, subscript R = 4.5) models plotted against porosity differences ranging from 0% to 50%. The shaded area indicates power reflectivity values of less than 0.01, a conservative estimate of the threshold at which background noise may preclude recording of reflection information (i.e., SNR less than or equal 1; Annan, 1996).

Perhaps most important to understanding GPR images, and the limits of GPR imaging, is understanding reflection coefficients at interfaces between rocks exhibiting different bulk dielectric constants. Equations (3) and (4) provide the framework for analyzing reflection-coefficient differences that result from differences in bulk dielectric constant predicted by mixing models. It is evident from fig. 7 that the influence of porosity differences is more pronounced when water occupies the pores. Based on the scenario shown in fig. 7, for single-fold data in dry sandstone, porosity differences between beds must exceed approximately 35% to obtain a reflection coefficient greater than 0.1 (fig. 11). Such a great difference is unlikely except between high porosity sands and shales or siltstones. If 128-fold stacking is performed, then a porosity difference of approximately 3 porosity percent can produce observable reflections. However, it requires an increase to approximately 1,024-fold stacking to lower the porosity difference threshold to approximately 1 porosity percent. As shown in figs. 6, 7, and 8, the presence of water in the pores can cause large changes to the reflection coefficient, if water is in one layer and not the other. If, however, water is present in both layers, then increasing water saturation can be associated with an initial decrease in reflection coefficient, requiring greater stacking to maintain signal-to-noise ratios. Worksheet 5 (appendix A) allows calculation of TWT and RC for three layers of specified properties and can be used to investigate RC changes in response to layer properties.



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Kansas Geological Survey
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