GPRInvNet: Deep Learning-Based Ground-Penetrating Radar Data Inversion for Tunnel Linings

A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data.

[1]  Amir Adler,et al.  Deep-learning tomography , 2018 .

[2]  Peng Jiang,et al.  Deep Learning Inversion of Electrical Resistivity Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Sébastien Lefèvre,et al.  Buried Object Detection from B-Scan Ground Penetrating Radar Data Using Faster-RCNN , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Yang Lei,et al.  Railway Subgrade Defect Automatic Recognition Method Based on Improved Faster R-CNN , 2018, Sci. Program..

[6]  David J. Daniels,et al.  Introduction to subsurface radar , 1988 .

[7]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Shinichiro Iso,et al.  Object Detection in Ground-Penetrating Radar Images Using a Deep Convolutional Neural Network and Image Set Preparation by Migration , 2018, International Journal of Geophysics.

[9]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[10]  Jacques R. Ernst,et al.  A New Vector Waveform Inversion Algorithm for Simultaneous Updating of Conductivity and Permittivity Parameters From Combination Crosshole/Borehole-to-Surface GPR Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Ludovic Métivier,et al.  Two-dimensional permittivity and conductivity imaging by full waveform inversion of multioffset GPR data: a frequency-domain quasi-Newton approach , 2014 .

[12]  H. Vereecken,et al.  GPR full-waveform inversion, recent developments, and future opportunities , 2018, 2018 17th International Conference on Ground Penetrating Radar (GPR).

[13]  Amir M. Alani,et al.  GPR Applications in Structural Detailing of a Major Tunnel Using Different Frequency Antenna Systems , 2018 .

[14]  Anthony G. Cohn,et al.  Real-Time Hyperbola Recognition and Fitting in GPR Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Bin Zhang,et al.  Specific evaluation of tunnel lining multi-defects by all-refined GPR simulation method using hybrid algorithm of FETD and FDTD , 2018, Construction and Building Materials.

[16]  Yanghua Wang.  Frequencies of the Ricker wavelet , 2015 .

[17]  Bin Zhang,et al.  Improving reconstruction of tunnel lining defects from ground-penetrating radar profiles by multi-scale inversion and bi-parametric full-waveform inversion , 2019, Adv. Eng. Informatics.

[18]  GPR modelling applied to vertical and horizontal resolution of buried objects , 2011, 2011 International Conference on Electromagnetics in Advanced Applications.

[19]  Francesco Benedetto,et al.  Advanced Ground Penetrating Radar Signal Processing Techniques , 2017, Signal Process..

[20]  Pedro Arias,et al.  Assessing the Applicability of Ground-Penetrating Radar to Quality Control in Tunneling Construction , 2016 .

[21]  Xuan Feng,et al.  Quantitative imaging for civil engineering by joint full waveform inversion of surface-based GPR and shallow seismic reflection data , 2017 .

[22]  R. Knight,et al.  Improving crosshole radar velocity tomograms: A new approach to incorporating high-angle traveltime data , 2007 .

[23]  Qianci Ren,et al.  Inverts permittivity and conductivity with structural constraint in GPR FWI based on truncated Newton method , 2018 .

[24]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[25]  George A. McMechan,et al.  Acquisition and processing of wide-aperture ground-penetrating radar data , 1992 .

[26]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[27]  A. Tarantola.  Inversion of seismic reflection data in the acoustic approximation , 1984 .

[28]  Craig Warren,et al.  Realistic FDTD GPR Antenna Models Optimized Using a Novel Linear/Nonlinear Full-Waveform Inversion , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[29]  V. Pérez-Gracia,et al.  Horizontal resolution in a non-destructive shallow GPR survey: An experimental evaluation , 2008 .

[30]  A. P. Annan,et al.  Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy , 1989 .

[31]  Ghassan AlRegib,et al.  Semi-supervised learning for acoustic impedance inversion , 2019, SEG Technical Program Expanded Abstracts 2019.

[32]  Jing Wang,et al.  Automatic Recognition of Highway Tunnel Defects Based on an Improved U-Net Model , 2019, IEEE Sensors Journal.

[33]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[34]  N. Ricker.  The Form and Laws of Propagation of Seismic Wavelets , 1951 .

[35]  Graham Parkinson,et al.  Ground Penetrating Radar Evaluation of Concrete Tunnel Linings , 2009 .

[36]  Peng Jiang,et al.  Deep-Learning Inversion of Seismic Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Matteo Pastorino,et al.  Advanced Inversion Techniques for Ground Penetrating Radar , 2017 .

[39]  Jing Wang,et al.  Application of ground penetrating radar to detect tunnel lining defects based on improved full waveform inversion and reverse time migration , 2019, Near Surface Geophysics.

[40]  Nathan Ida,et al.  Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review , 2020, Applied Computing and Informatics.

[41]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Tapan Mukerji,et al.  Convolutional neural network for seismic impedance inversion , 2018, GEOPHYSICS.

[43]  Harry Vereecken,et al.  Improved Characterization of Fine-Texture Soils Using On-Ground GPR Full-Waveform Inversion , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Jianzhong Zhang,et al.  Determination of Parameters of Subsurface Layers Using GPR Spectral Inversion Method , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Ioan Nicolaescu,et al.  Improvement of Stepped-Frequency Continuous Wave Ground-Penetrating Radar Cross-Range Resolution , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Prospección geofísica de alta resolución mediante geo-radar, aplicación a obras civiles , 1994 .

[47]  Zheng Zhou,et al.  VelocityGAN: Subsurface Velocity Image Estimation Using Conditional Adversarial Networks , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[48]  Sixin Liu,et al.  Application of pre-stack reverse time migration based on FWI velocity estimation to ground penetrating radar data , 2014 .

[49]  Peter Annan,et al.  A review of Ground Penetrating Radar application in civil engineering: A 30-year journey from Locating and Testing to Imaging and Diagnosis , 2017, NDT & E International.

[50]  Peng Jiang,et al.  DifNet: Semantic Segmentation by Diffusion Networks , 2018, NeurIPS.

[51]  Trey Ideker,et al.  pyNBS: a Python implementation for network-based stratification of tumor mutations , 2018, Bioinform..

[52]  Alberto Jardón,et al.  Past, present and future of robotic tunnel inspection , 2015 .

[53]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[54]  Sarath Kodagoda,et al.  Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[55]  Luc Courard,et al.  Detection of Defects in Concrete With Ground Penetrating Radar , 2011 .

[56]  Nenad Gucunski,et al.  An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks , 2018 .

[57]  Carlos Balaguer,et al.  Tunnel structural inspection and assessment using an autonomous robotic system , 2018 .

[58]  Jie Gao,et al.  Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks , 2017 .

[59]  Youzuo Lin,et al.  Inversionet: Accurate and efficient seismic-waveform inversion with convolutional neural networks , 2018, SEG Technical Program Expanded Abstracts 2018.

[60]  Garrey Learmonth,et al.  REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK < , 2011 .