A modelling framework for the preliminary assessment of tile drainage detection using ground-penetrating radar

Geophysical surveys, particularly with ground-penetrating radar (GPR), have been proven useful tools for the detection and mapping of tile drainage in agricultural fields (Wienken & Grenzdorffer, 2024). However, the success of a GPR survey for this purpose depends on both the characteristics of the tile drain pipes, such as their material, diameter, and depth – which are often poorly documented – as well as environmental conditions, such as soil texture and moisture content. Furthermore, these environmental conditions can be highly variable in space and dynamic over time, adding to the challenge of assessing in advance whether a GPR survey will be worth the investment.

To assess the likelihood of successfully detecting tile drainage networks before planning a field survey, we developed a synthetic modelling framework using the open-source software gprMax (Warren et al., 2016). The framework evaluates how selected parameters influence the GPR signal, focusing on the reflection contrast expected when the electromagnetic wave interacts with a drainpipe in a simplified one-dimensional (1D) model. Whether detection is possible is determined by comparing the simulated reflection contrast with a general noise threshold typical for a time-domain GPR system with a specified centre frequency. In this study, all synthetic modelling tests were performed for a GPR system with a centre frequency of 300 MHz.

We explored the sensitivity of the GPR signal to soil texture, soil moisture content, as well as the radius, depth, and filling of the drainpipe, considering a laterally homogeneous soil profile composed of one or two layers. The validity of the modelling framework was assessed by comparing the predicted detectability with the detection success/failure in two real field cases with sandy and clayey soil types. While the synthetic model predicted feasible detection for the sandy field, no clear contrasts were visible in the radargrams after basic processing. This suggests the need for further refinement of the synthetic model, such as incorporating more complex soil variations and a more detailed representation of the drainpipe structure. Nevertheless, the modelling framework provides useful guidelines for planning and designing GPR field surveys, without requiring extensive prior information on site conditions.

Further research is recommended to explore additional centre frequencies, more complex soil structures, and the incorporation of higher-dimensional approaches (2D or even 3D) to extend the current modelling framework. However, it should balance complexity with practical applicability, as real field conditions are never entirely predictable and models must simplify certain aspects due to incomplete knowledge.

References

Warren, C., Giannopoulos, A., & Giannakis, I. (2016). gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications, 209, 163–170. https://doi.org/10.1016/j.cpc.2016.08.020

Wienken, J. S., & Grenzdorffer, G. J. (2024). Non-invasive detection methods for subsurface drainage systems: A comparative review. Agricultural Water Management, 304, 109099. https://doi.org/10.1016/j.agwat.2024.109099