Buried Object Characterization Using Ground Penetrating Radar

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Ground Penetrating Radar (GPR) has a wide range of applications such as detection of buried mines, pipes and wires. GPR has been used as a near-surface remote sensing technique, and its working principle is based on electromagnetic (EM) wave theory. Here proposed data set is meant for data driven surrogate modelling based Buried Object Characterization. The considered problem of estimating geophysical parameters of a buried object is 2D. The training and testing scenarios include B-scan images (2D data), which contain 16 pairs of A-scan (concatenated forms of A-scans). Each A-scan is a time-varying normalized power amplitude signal obtained at the one point along the synthetic aperture. In other words, the scanning path length is 600. In addition, A-scan combination was presented as the A-scan ID according to points at the scanning path (400 mm). The data consists of 600 time-varying amplitudes and the A-scan ID (601x1). Here, the A-scan ID is an integer between 1 and 16. The training and testing datasets consist of 315 linearly samples scenarios, and 63 randomly selected scenarios, respectively, with the data acquired using a full wave EM simulator. Each data set contains 16 A scan signals (the total of 5040 and 1008 A-scans for training and test data sets respectively).

For more detailed information please check [1].


[1] R. Yurt, H. Torpi, P. Mahouti, A. Kızılay and S. Koziel, “Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models,” to be published in IEEE Access.


To briefly describe the data sets, training and test data sets are data matrices with the size of 5040´605 and 1008´605. In the data sets, 5040 (obtained from 315 different scenarios), and 1008 (obtained from 63 different scenarios) are the sample size of training and test data sets respectively. The features in the 1st and 601st columns are the input of the model while the Depth, Lateral Position, Radius of the object, and Water Content of the soil are present in between 602nd-605th features. As mentioned in Section II.B, although the water content of the soil is a variable, this feature is not given to the model since such an approach might not be feasible in practice due to the inhomogeneities in the examined area. Three data set pair for studied cases in this work is presented with the mentioned data set features above. (I) data without any noise (Train_Raw and Test_Raw files), (II) data with 20 dB SNR (Train_20dB and Test_20dB files), and (III) data with 30 dB SNR (Train_30dB and Test_30dB files) rate.