Adding realistic noise models to synthetic ground‐penetrating radar data

Abstract

Cost‐effective computing capabilities paved the road to use numerical modeling to develop advanced methods and applications of ground‐penetrating radar (GPR). Realistic synthetic data and the corresponding modeling 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 modeling tools include heterogeneous and frequency‐dependent material properties, complex structures and interface geometries as well as 3D 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 modeled data. We present an approach to include realistic noise scenarios as typically observed in GPR field data into the flow of modeling synthetic GPR data. In our approach, we extract the noise from recorded GPR traces and add it to the modeled GPR data via a convolution‐based process. We illustrate our methodology using a modeling exercise, where we contaminate a synthetic 2D 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 to generate synthetic GPR with realistic noise characteristics and further highlights the limitations of assuming pure white Gaussian noise models.This article is protected by copyright. All rights reserved