A Simple Model Generator for Radar Sounding Applications Based on Stochastic Media Model and gprMax

Abstract

Ground penetrating radar (GPR), or more generically called radar sounding, is an established technique for probing subsurface objects and sublayer structures. Simulating pre-constructed models and comparing with the real-world observations can contribute to retrieve the geometrical and physical properties of subsurface objects. However, most GPR simulations are limited to geometrically simple models with homogeneous physical properties of subsurface media which are generally unable to represent the real-world situations. In this paper, we develop a simple model generator by combining the stochastic media model and gprMax, an open-source software for electromagnetic simulations, to jointly generate models with geometrically random fragments, which are forward models with minimum human intervention. These easily prepared and simulated models can enrich the model training datasets which can be further utilized in machine learning for GPR retrieval algorithms.