Adding realistic noise models to synthetic ground-penetrating radar data

Cost-effective computing capabilities have paved the road for the use of numerical modelling to develop advanced methods and applications of ground-penetrating radar (GPR). Realistic synthetic data and the corresponding modelling techniques, respectively, should consider all subsurface and above-ground aspects that influence GPR wave propagation and the characteristics of recorded signals. Critical aspects that can be realized in modern GPR modelling tools include heterogeneous and frequency-dependent material properties, complex structures and interface geometries as well as three-dimensional antenna models, including the interaction between the antenna and the subsurface. However, realistic noise related to the electronic components of a GPR system or ambient electromagnetic noise is often not considered, or simplified by assuming a white Gaussian noise model which is added to the modelled data. We present an approach to include realistic noise scenarios as typically observed in GPR field data into the flow of modelling synthetic GPR data. In our approach, we extract the noise from recorded GPR traces and add it to the modelled GPR data via a convolution-based process. We illustrate our methodology using a modelling exercise, where we contaminate a synthetic two-dimensional GPR dataset with frequency-dependent noise recorded in an urban environment. Comparing our noise-contaminated synthetic data with field data recorded in a similar environment illustrates that our method allows the generation of synthetic GPR with realistic noise characteristics and further highlights the limitations of assuming pure white Gaussian noise models.

Automated summary

Cost-effective computing capabilities have made it possible to use numerical modeling in the development of advanced methods and applications for ground-penetrating radar (GPR). However, current methods often overlook the inclusion of realistic noise or rely on simplified Gaussian noise models. This study presents an approach to incorporate realistic noise scenarios into the modeling of synthetic GPR data, demonstrating its effectiveness in generating synthetic GPR data with realistic noise characteristics and highlighting the limitations of pure white Gaussian noise models.