Abstract
Soil materials can exhibit strongly dispersive properties in the operating frequency range of a physical system, and the uncertain parameters of the dispersive materials introduce uncertainties in the simulation result of propagating waves. It is essential to quantify the uncertainty in the simulation result when the acceptability of these calculation results is considered. To avoid performing thousands of full-wave simulations, an efficient surrogate model based on artificial neural networks (ANNs) is proposed in this paper, to imitate the concerned ground penetrating radar (GPR) calculation. With the autoencoder neural network to reduce the dimensionality of data, the surrogate model successfully predicts the outputs of the GPR calculation using a small number of training samples. The finite-difference time-domain method with the uniaxial perfectly matched layer is used to collect sampling data for the surrogate model. The process of constructing the surrogate model is presented in detail in this paper. The proposed surrogate model is demonstrated to be an attractive alternative to the full-wave GPR calculation due to its considerable advantage in terms of computational expense and speed.
Highlights
T HE numerical simulation is an alternative interpretation of wave propagation in ground penetrating radars (GPRs), and it relies on a set of input parameters which can affect the electromagnetic pulses and the survey of a target [1]
In [8], the authors propose an intrusive method which implements generalized polynomial chaos expansion into the auxiliary differential equation (ADE) finite-difference time-domain (FDTD) [6] to quantify uncertainty induced by uncertain parameters
TWO-DIMENSIONAL GPR SYSTEM MODELING DESCRIPTION Fig. 5 shows the 2-D GPR system used in the ADE-FDTD simulation and artificial neural networks (ANNs) modeling in this research
Summary
T HE numerical simulation is an alternative interpretation of wave propagation in ground penetrating radars (GPRs), and it relies on a set of input parameters which can affect the electromagnetic pulses and the survey of a target [1]. GPRs are important remote sensing tools in many fields such as civil engineering [2], landmine detection [3], and environmental applications [4]. It is of great importance for the study of numerical modeling of GPR systems. A lot of numerical methods have been employed for GPR system modeling [1]. The exact values of the inputs are always unknown, leading to the uncertainties in the output of the simulation [7]. Quantifying the uncertainty in the simulation result is an indispensable part in GPR calculation when the acceptability of the output is considered [8]
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