Abstract
We present a novel inversion approach using a neural network to locate subsurface targets and evaluate their backscattering properties from ground penetrating radar (GPR) data. The presented inversion strategy constructs an adaptive linear element (ADALINE) neural network, whose configuration is related to the unknown properties of the targets. The GPR data is reconstructed (compression) to fit the structure of the neural network. The constructed neural network works with a supervised training mode, where a series of primary functions derived from the GPR signal model are used as the input, and the reconstructed GPR data is the expected/target output. In this way, inverting the GPR data is the equivalent of training the network. The back-propagation (BP) algorithm is employed for the training of the neural network. The numerical experiments show that the proposed approach can return an exact estimation for the target’s location. Under sparse conditions, an inverted backscattering intensity with a relative error lower than 3% was achieved, whereas for the multi-dominating point scenario, a higher error rate was observed. Finally, the limitations and further developments for the inverting GPR data with the neural network are discussed.
Highlights
Ground penetrating radar as a near-surface remote sensing technique is widely used in geophysical, environmental, and civil engineering applications
We propose a ground penetrating radar (GPR) inversion approach based on the ability of the neural networks to fit any non-linear function with an arbitrary level of precision [12]
A Ricker wavelet with 1 GHz center frequency was set as the incident source for exciting the transmitting antenna, whereas the computational domain covers a 1.0 m × 0.4 m area that was truncated by Perfectly Matched Layer (PML) absorbing boundary condition (ABC)
Summary
Ground penetrating radar as a near-surface remote sensing technique is widely used in geophysical, environmental, and civil engineering applications. The rapid development of Artificial Neural Networks (ANNs) provides new solutions for solving remote sensing problems. Núñez-Nieto et al presented an automated landmine and unexploded ordnance (UXO) detection based on machine learning [10], while Lameri et al studied the landmine detection method using convolutional neural networks [11]. These approaches present typical ways of using neural networks, whereby the networks are trained with a number of samples, and are used to detect, identify, or classify specific targets and features from raw or processed GPR data
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