Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Doctor of Philosophy

dissertationRecent surface mass balance changes in space and time over the polar ice sheets need to be better constrained in order to estimate the ice-sheet contribution to sea-level rise. The mass balance of any ice body is obtained by subtracting mass losses from mass gains. In response to climate changes of the recent decades, ice-sheet mass losses have increased, making ice-sheet mass balance negative and raising sea level. In this work, I better quantify the mass gained by snowfall across the polar ice sheets; I target specific regions over both Greenland and West Antarctica where snow accumulation changes are occurring due to rising air temperature. Southeast Greenland receives 30% of the total snow accumulation of the Greenland ice sheet. In this work, I combine internal layers observed in ice-penetrating radar data with firn cores to derive the last 30 years of accumulation and to measure the spatial pattern of accumulation toward the southeast coastline. Below 1800 m elevation, in the percolation zone, significant surface melt is observed in the summer, which challenges both firn-core dating and internal-layer tracing. While firn-core drilling at 1500 m elevation, liquid water was found at ~20-m depth in a firn aquifer that persisted over the winter. The presence of this water filling deeper pore space in the firn was unexpected, and has a significant impact on the ice sheet thermal state and the estimate of mass balance made using satellite altimeters. Using a 400-MHz ice-penetrating radar, the extent of this widespread aquifer was mapped on the ground, and also more extensively from the air with a 750-MHz airborne radar as part of the NASA Operation IceBridge mission. Over three IceBridge flight campaigns (2011-2013), based on radar data, the firn aquifer is estimated to cover ~30,000 km2 area within the wet-snow zone of the ice sheet. I use repeated flightlines to understand the temporal variability of the water trapped in the firn aquifer and to simulate its lateral flow, following the gentle surface slope (< 1) and undulated topography of the ice sheet surface toward the ablation zone of the ice sheet. The fate of this water is currently unknown; water drainage into crevasses and at least partial runoff is inferred based on the analysis of radar profiles from different years. I also present results from a field expedition in West Antarctica, where data collection combined high-frequency (2-18 GHz) radar data and shallow (< 20 m) firn cores from Central West Antarctica, crossing the ice divide toward the Amundsen Sea. The radar-derived accumulation rates show a 75% increase (+0.20 m w.eq. y-1) of net snow accumulation from the ice divide, toward the Amundsen Sea for a 70-km transect, assuming annual isochrones being detected in the radar profile. On the Ross Sea side of the divide, with accumulation rates less than 0.25 m w.eq. y-1 and significant wind redistribution, only a multi-annual stratigraphy is detected in the radar profile. Using radar, I investigated the small-scale variability within a radius of ~1.5 km of one firn-core site, and I find that the averaged variation in accumulation-rate in this area is 0.1 m w.eq. y-1 in the upper 25-m of the firn column, which is 20% of the average accumulation rate

スパースアレイ・レーダにおける信号処理技術

Tohoku University佐藤源之課

Integrated Condition Assessment of Subway Networks Using Computer Vision and Nondestructive Evaluation Techniques

Subway networks play a key role in the smart mobility of millions of commuters in major metropolises. The facilities of these networks constantly deteriorate, which may compromise the integrity and durability of concrete structures. The ASCE 2017 Report Card revealed that the condition of public transit infrastructure in the U.S. is rated D-; hence a rehabilitation backlog of $90 billion is estimated to improve transit status to good conditions. Moreover, the Canadian Urban Transit Association (CUTA) reported 56.6 billion CAD in infrastructure needs for the period 2014-2018. The inspection and assessment of metro structures are predominantly conducted on the basis of Visual Inspection (VI) techniques, which are known to be time-consuming, costly, and qualitative in nature. The ultimate goal of this research is to develop an integrated condition assessment model for subway networks based on image processing, Artificial Intelligence (AI), and Non-Destructive Evaluation (NDE) techniques. Multiple image processing algorithms are created to enhance the crucial clues associated with RGB images and detect surface distresses. A complementary scheme is structured to channel the resulted information to Artificial Neural Networks (ANNs) and Regression Analysis (RA) techniques. The ANN model comprises sequential processors that automatically detect and quantify moisture marks (MM) defects. The RA model predicts spalling/scaling depth and simulates the de-facto scene by developing a hybrid algorithm and interactive 3D presentation. In addition, a comparative analysis is performed to select the most appropriate NDE technique for subway inspection. This technique is applied to probe the structure and measure the subsurface defects. Also, a novel model for the detection of air voids and water voids is proposed. The Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Monte Carlo Simulation (MCS) are streamlined through successive operations to create the integrated condition assessment model. To exemplify and validate the proposed methodology, a myriad of images and profiles are collected from Montréal Metro systems. The results ascertain the efficacy of the developed detection algorithms. The attained recall, precision, and accuracy for MM detection algorithm are 93.2%, 96.1%, and 91.5% respectively. Whereas for spalling detection algorithm, are 91.7%, 94.8%, and 89.3% respectively. The mean and standard deviation of error percentage in MM region extraction are 12.2% and 7.9% respectively. While for spalling region extraction, they account for 11% and 7.1% respectively. Subsequent to selecting the Ground Penetrating Radar (GPR) for subway inspection, attenuation maps are generated by both the amplitude analysis and image-based analysis. Thus, the deteriorated zones and corrosiveness indices for subway elements are automatically computed. The ANN and RA models are validated versus statistical tests and key performance metrics that indicated the average validity of 96% and 93% respectively. The air/water voids model is validated through coring samples, camera images, infrared thermography and 3D laser scanning techniques. The validation outcomes reflected a strong correlation between the different results. A sensitivity analysis is conducted showing the influence of the studied subway elements on the overall subway condition. The element condition index using neuro-fuzzy technique indicated different conditions in Montréal subway systems, ranging from sound concrete to very poor, represented by 74.8 and 35.1 respectively. The fuzzy consolidator extrapolated the subway condition index of 61.6, which reveals a fair condition for Montréal Metro network. This research developed an automated tool, expected to improve the quality of decision making, as it can assist transportation agencies in identifying critical deficiencies, and by focusing constrained funding on most deserving assets

High-resolution imaging of transport processes with GPR full-waveform inversion

Imaging subsurface small-scale features and monitoring transport of tracer plumes at a fine resolution is of interest to characterize transport processes in aquifers. Full-waveform inversion (FWI) of crosshole ground penetrating radar (GPR) measurements enables aquifer characterization at decimeter-scale resolution. GPR FWI provides 2D tomograms of the subsurface properties, the dielectric permittivity (ε) and electrical conductivity (σ), which can be correlated with hydrological properties. In the framework of the thesis, we conducted synthetic and experimental tracer tests that were monitored using time-lapse crosshole GPR full-waveform inversion results, to test the potential and limitation to reconstruct the tracer plume. For the synthetic test, we generated a realistic high resolution aquifer model based on previous hydrological and GPR FWI data from the Krauthausen test site in order perform a transport simulation that represents reasonable heterogeneity of the tracer concentration. Using petrophysical relations, we converted the concentration distribution to dielectric properties of specific tracers: saltwater (increase σ only), desalinated water (decrease σ only) and ethanol (decrease in both σ and ε). One important aspect of the GPR FWI is to investigate an optimal way to define adequate starting models especially for the time-lapse data. Therefore, we investigated three different starting model options in the synthetic test, resulting that ε and σ models from the background provide the most accurate FWI of time-lapse data. Hereby, both ε and σ FWI results have shown the potential to derive time-lapse changes. The gained insights of the synthetic optimization tests are applied for an experimental test. To prove the potential of the crosshole GPR FWI also under realistic conditions, we performed an experimental salt tracer experiment at the Krauthausen test site. Thereby, we injected to the sandy aquifer a salt tracer, and monitored the tracer development using crosshole GPR over a timeframe of 14 days within 5 crosshole planes in an area of 11x10 m. These time-lapse data are independently inverted using the background models of each plane as starting models as proposed from the synthetic study to derive the best FWI results. We investigated the consistency of the reconstruction of the plume by temporal and spatial continuity across neighboring planes, by correlating with borehole logging data, and with expectations based on previous tracer experiments from the same site. One challenge arise from the time-lapse GPR data caused by the change of the borehole filling properties over the time and transport of the plume. The salt and freshwater mixture in the tubes couple with the borehole antennae thus influence the GPR data. Fortunately, the processing for the FWI enables accounting this effect by estimating effective source wavelets for each time step and each plane, which compensate for borehole filling effects caused by the salt tracer. If these borehole filling effects would not be considered, errors in the results would occur. Performing the FWI considering the corrected effective source wavelets allows recovery of the aquifer models independently from saltwater-antennae effects. Such effects cannot be incorporated using standard ray-based approaches. In contrast from the synthetic tracer test, investigation of the best starting model for experimental data showed that σ homogenous model rather than from FWI background provides more accurate results for FWI of time-lapse data. This can be explained that possible errors in the FWI background results caused by measurement or starting model uncertainties, are forcing the FWI with these models to be trapped in a local minimum. The time-lapse GPR FWI has shown a reliable manifestation of a tracer of about 0.2 m resolution, which was not observed before from other geophysical monitoring techniques. These improved and higher resolution images of such a tracer transport can help in future to better constraint hydrological properties of interest for hydrological models. In this thesis, we have shown for the first time the potential of the GPR FWI to characterize and monitor tracer experiments using crosshole GPR data. Especially, the application to salt tracers, which traditionally were investigated with ERT, is now also possible with GPR and higher resolution images of the tracer transport are possible to obtain