Size estimation of underground targets from GPR frequency spectra: A deep learning approach

Combined application of GPR and ERT for the assessment of a wall structure at the Heptapyrgion fortress (Thessaloniki, Greece)

In Big data era, the data volume produced by ground penetrating radar (GPR) application swelling and has the characteristics of heterogeneity. This paper introduces the current status of the big data. In view of the characteristics of ground penetrating radar (GPR) application used in road disease detection, we design and realize a kind of ground penetrating radar (GPR) data management system GRBase based on Hadoop platform used in road disease detection, propose and implement a GPR data preprocessing method based on the Storm, solve the problem that the GPR data and Hadoop input format does not match. GRBase uses MySQL and HBase as storage database and enhances the radar data computational efficiency in road disease detection applications. The experiments and practice demonstrate that GRBase management platform is an effective, practical and strong scalability distributed GPR data management system.

The objective of this work was to evaluate the feasibility and value of expanding the MDT's Ground Penetrating Radar (GPR) program to pavement design and rehabilitation, and to network level evaluation. Phase I of this project concluded that in order to investigate the feasibility and value of these program expansions, a Phase II field evaluation project be designed and implemented to evaluate the accuracy of GPR pavement thickness data on Montana pavements, and to correlate these findings with the accuracy requirements of the individual applications. This field evaluation began with identifying 26 pavement test section of different composition and structure located throughout the diverse climatic regions of Montana. At each site, FWD and GPR data was collected, followed by coring and augering to determine the thickness of the pavement layer structure and base moisture content. This testing was carried out both in the spring of 2010 and in the fall of 2010 to capture seasonal variations. The GPR data was analyzed for thickness, and the GPR thickness data was evaluated for seasonal changes, and compared to core and plan data to investigate thickness accuracy and the effectiveness of calibration methods. Compared to cores, the average GPR bound layer thickness error was 10.3% vs. 15.2% using plan data. A GPR data checking method was developed using FWD and plan data to identify potential analysis layer analysis inconsistencies and suggest alternative interpretation. Implementation of this method reduced the GPR error to 7.6%. A sensitivity study was carried out to investigate the impact of having the more accurate GPR data. This study showed that, on average, the use of GPR reduced the pavement life prediction error by 62% when compared to using as-built plan data.

Characterization of inland water is a topic of great interest due to the broad spectrum of potential applications. Applied geophysic boasts different techniques adapted to retrieve useful information about these kinds of environments. Certainly, the most common geophysical techniques used in shallow waters are the seismic methods. However, there are some situations in which seismic methods could fail. Although nowadays it does not exist a method able to solve completely this task, electromagnetic techniques are a cost efficient tool to provide useful information. Thanks to their versatility, we concentrated our attention on the possibility of the Ground Penetrating Radar (GPR) and of the low induction number electromagnetic multi-frequency soundings measurements, carried from the water surface. We started from acquisitions performed in controlled settings. We described how we reproduced the field condition of a riverine GPR survey in laboratory experimentation. We selected a 1500 MHz GPR antenna, and we studied five types of riverine bottom sediments. We developed two different approaches to interpret the GPR responses of the sediments: the velocity and the amplitude analysis. The amplitude analysis developed is particularly innovative and fit very well the field requirements. We tried to estimate the sediments porosities by some mixing rules by the electromagnetic properties founded with both the analysis performed. The comparison among the porosities provided by the GPR measurements and the porosities measured by direct methods confirm the accuracy of the velocity analysis and it highlights the poor reliability of the amplitude analysis. Successively, we tested our methodology in survey condition. We conducted an integrated geophysical campaign on a stretch of the river Po in order to check the GPR ability to discriminate the variability of riverbed sediments through an analysis of the bottom reflection amplitudes. We conducted continuous profiles with a 200MHz GPR system and a handheld broadband electromagnetic sensor. A conductivity meter and a TDR provided punctual measurements of the water conductivity, permittivity and temperature. The processing and the interpretation of both the GEM-2 and GPR data were enhanced by the reciprocal results and by integration with the punctual measurements of the electromagnetic properties of the water. The GPR measurements provided maps of the bathymetry and of the bottom reflection amplitude. The high reflectivity of the riverbed, derived from the GPR interpretation, agreed with the results of the direct sampling campaign that followed the geophysical survey. The variability of the bottom reflection amplitudes map, which was not confirmed by the direct sampling, could also have been caused by scattering phenomena due to the riverbed clasts which are dimensionally comparable to the wavelength of the radar pulse. About the multi-frequency electromagnetic sensor, we analyzed the induction number, the depth of investigation (DOI) and the sensitivity of our experimental setup by forward modeling varying the water depth, the frequency and the bottom sediment resistivity. The simulations led to an optimization of the choice of the frequencies that could be reliably used for the interpretation. The 3406 Hz signal had a DOI in the PO water (27 Ωm) of 2.5m and provided sediment resistivities higher than 100 Ωm. We applied a bathymetric correction to the conductivity data using the water depths obtained from the GPR data. We plotted a map of the river bottom resistivity and compared this map to the results of a direct sediment sampling campaign. The resistivity values (from 120 to 240Ωm) were compatible with the saturated gravel with pebbles in a sandy matrix that resulted from the direct sampling, and with the known geology

Portugal, in the continental and insular territory, has raw geological resources in diversity and quality that are materials for the manufacturing industries, particularly linked to the civil construction, architecture and public works with great weight in its exports, revealing to be one big producer of ornamental stones worldwide. In this industry, the presence of discontinuities, grain size, colour, textural anisotropy and porosity are factors that can determine the economic viability of the exploitations. For this, it is important to develop effective prospecting routines that allow the geological/structural fast analysis and economic potential assessment of the massifs and subsequent correct planning and dimensioning of the exploitations. This work intends to model and integrate data from the ground penetration radar (GPR), together with close-range photogrammetry, derived from an Unmanned aerial vehicle (UAV) imagery. The objective is to identify and determine the spatial distribution of the various elements by modelling the acquired data, as well as verifying the feasibility of the technical disassembly option adopted. We have used GPR as it is non-destructive, fast to deploy, survey, process and interpret. The acquired data were processed using the GPR-SLICE, where a 3D final dataset was obtained and interpreted. GPR and photogrammetric model was integrated and interpreted and validated with direct field observations. The model obtained showed in its upper part, an area corresponding to the presence of sludge from the cut of the rock, followed by a strip that corresponds to the oolitic limestone. Further down to the end of the block, the presence of oolitic limestone with crossed stratification is identifiable. There was a textural and structural correspondence between the GPR data and direct field observations. GPR did not reveal any major morphostructural discontinuity, validating the technical option of choosing the places where the cuts were made for their individualization, as the block was cut clean. The GPR and photogrammetry data integration method revealed to be complementary, where results were obtained easily, fast, and with centimeter accuracy. The same methodology presented, revealed to be cheap and effective for both localized studies and optimization of the overall quarry’s extraction plan and design.

In this paper, we describe the application of wave-equation redatuming through downward and upward continuations of surface ground penetrating radar (GPR) data to image the target beneath a surface layer with geometric or material property heterogeneities. We first tested this technique with the use of a synthetic radar profile generated by the finite difference time domain method to show its effectiveness. We then applied it to the surface GPR data collected in several urban public transportation infrastructure sites including a project to use GPR to assess the quality of mortar consolidation behind a tunnel wall in the Xiangyin highway tunnel, Shanghai, China. The purpose of this redatuming processing is to enhance target images in later times of a GPR profile, by eliminating the contamination in the GPR profile caused by strong diffractive scattering from the steel rebars in the surface concrete slab or tunnel wall. The results for synthetic and observed GPR data show that the wave-equation redatuming technique is an effective way to eliminate unwanted diffraction signatures and ease GPR image interpretation for data collected at a site with a strongly heterogeneous surface layer.

Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similarity within ground-penetrating radar (GPR) profiles. We have observed that the intricate and often visually indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar subsurface characteristics. In this study, we therefore examined the feasibility of using textural features extracted from GPR data to automate subsurface characterization. The textural features were matched to a fingerprint database of previous subsurface classifications of GPR textural features and the corresponding physical probings of subsurface conditions. Four textural features (energy, contrast, entropy, and homogeneity) were selected as inputs into a neural-network classifier. This classifier was tested and verified using GPR data obtained from two distinctly different field sites. The first data set contained features that indicate the presence or lack of sandstone bedrock in the upper 2 m of a shallow soil profile of fine sandy loam and loam. The second data set contained columnar patterns that correspond to the presence or the lack of vertical preferential flow paths within a deep loessial soil. The classifier automatically grouped each data set into one of the two categories. Comparing the results of classification using extracted textural features to the results obtained by visual interpretation found 93.6% of the sections that lack sandstone bedrock correctly classified in the first set of data, and 90% of the sections that contain pronounced columnar patterns correctly classified in the second set of data. The classified profile sections were mapped using integrated GPR and GPS data to show ground surface boundaries of different subsurface conditions. These results indicate that textural features extracted from GPR data can be utilized as inputs in a neural network classifier to rapidly characterize and map the subsurface into categories associated with known conditions with acceptable levels of accuracy. This approach of GPR imagery classification is to be considered as an alternative method to traditional human interpretation only in the classification of voluminous data sets, wherein the extensive time requirement would make the traditional human interpretation impractical.

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusion of hyperspectral and GPR data. In contrast, the combination of simulated, sensor-like soil-moisture values and hyperspectral data achieves the worst regression performance. In conclusion, the estimation of soil moisture with hyperspectral and GPR data engages further investigations.

Full-wave modeling of ground-penetrating radar (GPR) data using Green’s functions for wave propagation in planar layered media and antenna characteristic global reflection and transmission functions for describing far-field antenna effects, including antenna-medium interactions, has shown a great potential for nondestructive characterization of soils and materials. The accuracy of the retrieved parameters in the GPR data inversion depends on the accuracy of the GPR external calibration. In this research we studied the stability and the repeatability of two different GPR systems, namely, frequency- and timedomain systems. A combination of a vector network analyzer and 800–5200 MHz horn antenna was used as a frequency-domain GPR (FD-GPR) whereas a GSSI GPR system using a 900 MHz bowtie antenna was used as a time-domain GPR (TD-GPR). Both GPR systems including their antennas were calibrated several times using measurements with the antennas at different heights over a perfect electric conductor (PEC) in the laboratory as well as over a water layer. In addition, measurements were performed over a thin water layer and a relatively thick sandy soil layer as validating medium. The results showed that the FD-GPR is relatively stable while the TD-GPR presents a significant drift which can be accounted for using corrections based on the air direct-coupling waves (free-space measurements). Water- and PEC-based calibrations provided very similar results for the GPR calibration functions. Inversions for the water layer and the sandy soil layer provided reliable results and showed a high degree of the repeatability for both radar systems. The error on the calibration based on inaccurate antenna heights over PEC showed the significant errors on the inversion results for the directive antenna (horn antenna) but less error for the bowtie antenna. This analysis demonstrated the general validity of the proposed far-field radar modeling approach, not only with respect to frequency and time domain radars but as well with respect to the calibrating medium.

We investigated a significant correlation between Ground-Penetrating Radar (GPR) combined with down-hole seismic technique and geotechnical data (boring log and Standard Penetrating Test) to examine geophysical data as an important aid tool in interpreting geotechnical parameter. GPR data provide geotechnical engineers with continuous layering information, whereas down-hole seismic test (DHT) delivers geotechnical parameters such as Poisson's ratio $\sigma$ , Young Modulus E, Shear modulus and Bulk moduli. By combining these data with boring log and standard penetrating Test (SPT) data, a comprehensive estimation about geotechnical parameters can be achieved. We conducted GPR measurement with 100 MHz shielded antenna in two profiles (inline and cross-line) centered at a borehole. We conducted also SPT to measure soil strength and boring to know the lithology. Intensive examination reveals that GPR technique should be applied to extend the meaning of geotechnical parameter value around and between boreholes. From case study at ITB site, we found some interesting result. Whereas both GPR and down-hole seismic data can reveal hard soils with SPT value of more than 60 corresponding to silty sand, GPR data can detect the interface between silty clay and clayey silt layers that cannot be detected from SPT. This study reveal the potential use of GPR technique as a tool for investigation of subsurface that can be elaborated to the exploration of shallow coal seams in subsurface as well to that of valuable minerals.

Improving ground-penetrating radar data in sedimentary rocks using deterministic deconvolution

Improving understanding of Arctic ecosystem climate feedback and parameterization of models that simulate freeze-thaw dynamics require advances in quantifying soil and snow properties. Due to the significant spatiotemporal variability of soil properties and the limited information provided by point-scale measurements (e.g., cores), geophysical methods hold potential for improving soil and permafrost characterization. In this study, we evaluate the use of a ground-penetrating radar (GPR) to estimate thaw layer thickness, snow depth, and ice-wedge characteristics in an ice-wedge-dominated tundra region near Barrow, AK, USA. To this end, we analyze GPR and point-scale measurements collected along several parallel transects at the end of the growing season and the end of frozen season. In addition, we compare the structural information extracted from the GPR data with electrical resistivity tomography (ERT) information about ice-wedge characteristics. Our study generally highlights the value of GPR data collected in the frozen season, when conditions lead to the improved GPR signal-to-noise ratio, facilitate data acquisition, and reduce acquisition-related ecosystem disturbance relative to growing season. We document for the first time that GPR data collected during the frozen season can provide reliable estimates of active layer thickness and geometry of ice wedges. We find that the ice-wedge geometry extracted from GPR data collected during the frozen season is consistent with ERT data, and that GPR data can be used to constrain the ERT inversion. Consistent with recent studies, we also find that GPR data collected during the frozen season can provide good estimates of snow thickness, and that GPR data collected during the growing season can provide reliable estimate thaw depth. Our quantification of the value of the GPR and ERT data collected during growing and frozen seasons paves the way for coupled inversion of the datasets to improve understanding of permafrost variability.

Unsaturated flow phenomena, such as unstable wetting fronts and preferential flow, cannot be investigated using small‐scale sampling. Dye tracer experiments can help visualize the dynamics of water flow but are destructive and therefore irreproducible. We investigated the applicability of high‐resolution ground penetrating radar (GPR) for nondestructive visualization of unsaturated flow patterns arising from a forced infiltration experiment. Synthetic studies using a reflection GPR two‐dimensional finite difference time domain modeling code indicated that differences in water content caused by preferential flow and fingering could be resolved. Moisture content contrasts down to approximately 2.5% within the top 2 m were detectable, but with increasing degrees of heterogeneity in the subsurface it becomes difficult to distinguish these moisture content changes. We conducted a field experiment in which 100 mm (900 L) of Brilliant Blue dyed water was infiltrated across a 3‐ by 3‐m area in relatively homogenous and undisturbed sandy alluvial sediments. Reflection GPR data were collected before and after infiltration. Dye‐staining patterns, revealed by excavating a 2‐m‐deep trench through the infiltration area, were compared with changes in the GPR data. Reflection amplitude changes as well as reflection delay revealed significant differences within the dye‐stained area. The GPR data provided information about the unsaturated flow below the extent of the dye staining, and the results of the synthetic GPR modeling, as well as the observed changes in the real GPR data set, underline the potential of reflection GPR as a nondestructive method to map unsaturated flow phenomena.

Accurate assessment of the quality of concrete bridge decks and identification of corrosion induced delamination leads to economic management of bridge decks. It has been demonstrated that ground penetrating radar (GPR) can be successfully used for such purposes. The growing demand on GPR has brought into the challenge of developing automatic processes necessary to produce a final accurate interpretation. However, there have been few publications targeting at automatic detection of bridge deck delamination from GPR data. This paper proposes a novel method using partial differential equations (PDEs) to detect rebar (or steel-bar) mat signatures of concrete bridges from GPR data so that the delamination within the bridge deck can be effectively located. The proposed algorithm was tested on both synthetic and real GPR data and the experimental results have demonstrated its accuracy and reliability, even for diminished contrast and low signal-to-noise ratio.

Resolution enhancement of deconvolved ground penetrating radar images using singular value decomposition