A Machine Learning Approach For Simulating Ground Penetrating Radar

Giannakis, Iraklis, Giannopoulos, Antonios and Warren, Craig (2018) A Machine Learning Approach For Simulating Ground Penetrating Radar. In: 2018 17th International Conference on Ground Penetrating Radar (GPR). IEEE, pp. 1-4. ISBN 978-1-5386-5778-2

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Abstract

The ability to produce, store and analyse large amounts of well-labeled data as well as recent advancements on supervised training, led machine learning to gain a renewed popularity. In the present paper, the applicability of machine learning to simulate ground penetrating radar (GPR) for high frequency applications is examined. A well-labelled and equally distributed training set is generated synthetically using the finite-difference time-domain (FDTD) method. Special care was taken in order to model the antennas and the soils with sufficient accuracy. Through a stochastic parameterisation, each model is expressed using only seven parameters (i.e. the fractal dimension of water fraction, the height of the antenna and so on). Based on these parameters and the synthetically generated training set, a machine learning framework is trained to predict the resulting A-Scan in real-time. Thus, overcoming the time-consuming calculations required for an equivalent FDTD simulation.

Item Type:	Book Section
Uncontrolled Keywords:	FDTD, GPR, machine learning, neural networks
Subjects:	H100 General Engineering
Department:	Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User:	Becky Skoyles
Date Deposited:	25 Sep 2018 10:09
Last Modified:	11 Oct 2019 19:15
URI:	http://nrl.northumbria.ac.uk/id/eprint/35886

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