Application of ground penetrating radar to detect tunnel lining defects based on improved full waveform inversion and reverse time migration

Abstract

Ground penetrating radar is a popular approach to detect defects in tunnel lining. However, the interpretation is usually based on the original image, which is very different from the real shape of the lining defects. Full waveform inversion and reverse time migration are helpful to solve this problem. Full waveform inversion can invert the relative permittivity distribution and reverse time migration can migrate reflection events to their proper locations. Traditional full waveform inversion method is only applicable to cross‐hole ground penetrating radar data or surface multi‐offset ground penetrating radar data. We propose an improved full waveform inversion method which offers satisfactory inversion result for surface common‐offset radar. The forward modelled waveform and the objective function curve show that our new full waveform inversion method is much more accurate than traditional full waveform inversion for common‐offset radar. Traditional reverse time migration has weaker amplitude with increasing depth; we use an energy matrix to improve the imaging effect. Moreover, our reverse time migration is based on the relative permittivity distribution obtained from full waveform inversion, which provides more accurate imaging result. Through several numerical and engineering examples, we discuss the application of both methods in tunnel lining inspection. The results show that for tunnel lining without rebars, the combined methods can give satisfactory imaging results. But the image quality deteriorates rapidly when dealing with rebars.

Combined Migrations and Time-Depth Conversions in GPR Prospecting: Application to Reinforced Concrete

“…It has been imported historically from seismics [10,11] but can also be mathematically derived from Maxwell's equations [12] and in particular from the theory of diffraction tomography [13][14][15]. It has been exploited for and/or adapted to many specific applications [16][17][18][19], and any meaningful list of case histories would be beyond our purposes.…”

Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network

“…Commonly used theoretical methods are migration imaging and inversion calculation, which can obtain the relative dielectric constant model. Researchers have conducted comprehensive work on this [12,13] . In addition, much research exists regarding automatic identification of anomalous objects in GPR data based on the pattern recognition and machine learning methods.…”

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

“…FWI originated in the field of seismic exploration [21] and has been rapidly employed for processing radar data since [22]. In tunnel liningrelated applications, some developments of FWI have been presented to further improve performance, including a truncated Newton method based on GPR FWI with structural constraints [23], a multi-scale inversion strategy and biparametric FWI method [24], and a combination of improved FWI and RTM [25]. However, because tunnel lining defects always have irregular geometries and complex distributions, the received subsurface GPR data are generally interlaced and accompanied by discontinuous and distorted echoes.…”