Improving the Autoregressive Modeling Method in Random Noise Suppression of GPR Data Using Undecimated Discrete Wavelet Transform

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.

Attenuation analysis of real GPR wavelets: The equivalent amplitude spectrum (EAS)

For the past decade, texture analysis has become one of the quantitative attributes used extensively in seismic studies for target detection and interpretation of subsurface anomalies, such as moisture content and landmines in inhomogeneous soil. The application of this analysis has been limited to other geophysical techniques, such as Ground Penetrating Radar (GPR). GPR is a non-invasive method based on the propagation of electromagnetic waves to derive a model for the subsurface. In general, interpreting GPR data is primarily qualitative and depends on the expertise of the analysts. The goal of this study is to verify the ability of texture analysis technique to differentiate soil mineralogy. We have developed and tested a Matlab code that derives various texture measures such as energy, homogeneity, contrast, and entropy. Those statistical measures are generated using a gray level co-occurrence matrix (GLCM). The measures supply different information about the data, such as uniformity or complexity; thus, they can produce different features in the GPR data when they are used together. We tested the texture analysis code on synthetic GPR data sets that have been derived for two different models with two different heavy minerals, ilmenite and spodumene embedded inside a host medium. To obtain synthetic data, we used GPRMax2D software which applies the Finite Difference Time-Domain method (FDTD) to simulate various subsurface scenarios. In addition to the synthetic data, real GPR data that had been collected from a prototype laboratory experiment were used. The calculated statistical features and results show that ilmenite has higher entropy, dissipation, and contrast measures than spodumene. On the other hand, spodumene shows higher energy and homogeneity features than ilmenite. Based on the synthetic data results, the combination of the texture-based analysis measures can be used as an enhanced interpretation tool that clearly brings out the distinction between minerals. The texture results computed from ground-truth GPR data show that heavy mineral bodies can be identified due to their high contrast, entropy, correlation, standard deviation, and low energy and homogeneity. Variance measure of texture analysis can helphighlight the edge of the buried samples. Cluster indicator is more effective in visualizing the anomaly than the raw data.

This paper presents a parametric study on the influence of target and soil properties, including depth of burial, on features extracted from ground penetrating radar (GPR) data. Understanding this influence is crucial for designing a classifier that uses these features for mine detection and identification. Two types of features have been studied. These are the Wigner-Ville distribution and geometric moments. Using a fast forward modeling program, synthetic GPR data were created for six buried objects, including two plastic minelike objects, for a wide range of soil properties and depths of burial. Both non-lossy and lossy soils were considered. From the computed data the above features were extracted and correlated with each other. The results show that the Wigner-Ville distribution performs much better in discriminating between objects than geometric moments. Furthermore, the features were found to be practically invariant to changes in mine-soil permittivity contrast and depth of burial provided that the soil is non-lossy. In the presence of losses, the GPR pulse is reshaped at the air-ground interface and as it propagates through the soil. As a result of the reshaping, the target response and hence the features can differ substantially from the non-lossy case.

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.

<p>The most frequently used survey mode for acquiring Ground Penetrating Radar (GPR) data is common offset (CO) – where a single transmitter and receiver pair move along a survey line at a constant (offset) separation distance. This allows rapid and dense data acquisition, and therefore high-resolution large-scale investigations, to be carried out with relative ease, and at relatively low cost. However, it has long been known that multi-offset survey methods, such as common mid-point (CMP) and wide-angle reflection-refraction (WARR), can offer many benefits over CO: detailed subsurface EM wave velocity models; enhanced reflection sections with higher signal-to-noise ratio (SNR); the potential to adapt well-established advanced seismic processing schemes for GPR data [1-2].</p><p>Despite the advantages of multi-offset GPR data, these methods have seen limited adoption as, in the past, they required significantly more time, effort, and hence cost, to collect. However, recent advances in GPR hardware, particularly in timing and control technology, have enabled the development of multi-concurrent sampling receiver GPR systems such as the “WARR Machine” manufactured by Sensors & Software Inc. [3-4]. These newly developed GPR systems have the potential to provide all the aforementioned benefits with considerably less effort and therefore reduced survey cost, as they allow for the fast acquisition of multi-offset WARR soundings.</p><p>In this work, we look at the challenges and opportunities from collecting and processing multi-offset GPR data. We demonstrate a processing workflow that combines standard GPR processing approaches, with methods adapted from seismic processing, as well as our own algorithms. This processing framework has been implemented into a GUI-based software written in MATLAB [5], and has been tested using both synthetic [6] and real multi-offset GPR data. Some of the specific challenges with multi-offset GPR that we investigate are time zero misalignments, CMP balancing, velocity analysis, and automated velocity picking. We show how addressing these issues can result in improved velocity analysis, and ultimately in improved subsurface velocity models, and stacked sections.</p><p><strong>References</strong></p><p>[1] Ursin, B., 1983. Review of elastic and electromagnetic wave propagation in horizontally layered media. Geophysics, 48(8), pp.1063-1081.</p><p>[2] Carcione, J. and Cavallini, F., 1995. On the acoustic-electromagnetic analogy. Wave Motion, 21(2), 149-162.</p><p>[3] Annan, A. P., and Jackson, S., 2017. The WARR machine. 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR).</p><p>[4] Diamanti, N., Elliott, J., Jackson, R. and Annan, A. P., 2018, The WARR Machine: System Design, Implementation and Data: Journal of Environmental & Engineering Geophysics, 23, pp.469-487.</p><p>[5] Angelis, D., Warren, C. and Diamanti, N., 2020. A software toolset for processing and visualization of single and multi-offset GPR data. 18th International Conference on Ground Penetrating Radar.</p><p>[6] Warren, C., Giannopoulos, A. and Giannakis, I., 2016. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications, 209, pp.163-170.</p>

In ground penetrating radar (GPR) records, because of different kinds of noises, especially direct wave of ground surface, the real target is not seen clearly in the radar echo section, which makes the detection of real targets difficult. Considering that the direct waves among traces are not change greatly and have strong correlation, so it can be restrained by certain methods. KL transform often be used to pick up coherent noises and random noises in multi channel seismic records[U 4]. In this paper, KL transform is used to GPR data processing, which fully uses the different second-order statistic characteristics of their correlations on the direct wave, target signals in multi channel GPR data. The target signals are successfully separated form noises according to the corresponding relation of the eigenvalue's magnitude of the correlation matrix and correlation strength of each component in the multi channel GPR data.

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.

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.

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.

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.

'The project''s goals are threefold: (1) to examine the complementary site-characterization capabilities of modern, three-component shallow-seismic techniques and ground-penetrating radar (GPR) methods at depths ranging from 2 to 8 m at an existing test site; (2) to demonstrate the usefulness of the two methods when used in concert to characterize, in three-dimensions, the cone of depression of a pumping well, which will serve as a proxy site for fluid-flow at an actual, polluted site; and (3) to use the site as an outdoor mesoscale laboratory to validate existing three-dimensional ground-penetrating radar and seismic-reflection computer models developed at the Univ. of Kansas. To do this, useful seismic and GPR data are being collected along the same line(s) and within the same depth range. The principal investigators selected a site in central Kansas as a primary location and, although the site itself is not environmentally sensitive, the location chosen offers particularly useful attributes for this research and will serve as a proxy site for areas that are contaminated. As part of an effort to evaluate the strengths of each method, the authors will repeat the seismic and GPR surveys on a seasonal basis to establish how the complementary information obtained varies over time. Because the water table fluctuates at this site on a seasonal basis, variations in the two types of data over time also can be observed. Such noninvasive in-situ methods of identifying and characterizing the hydrologic flow regimes at contaminated sites support the prospect of developing effective, cost-conscious cleanup strategies in the near future. As of the end of May 1998, the project is on schedule. The first field work was conducted using both of the geophysical survey methods in October of 1997, and the second field survey employed both methods in March of 1998. One of the stated tasks is to reoccupy the same survey line on a quarterly basis for two years to examine change in both the seismic reflection data and the ground-penetrating radar (GPR) data over time. Two factors drive these changes: First, the soil-moisture conditions vary on a seasonal basis at the site. Second, the water table rises and falls on the order of one meter in response to changes in the level of the Arkansas River and in response to the many irrigation wells found nearby. At the test site in the Arkansas River alluvial valley near Great Bend, Kansas, surface material consists of unconsolidated medium- to coarse-grained sand interspersed with clay stringers and lenses deposited by the Arkansas River. A hand-augered test hole 5 meters from the seismic line revealed sand to a depth of about 1.5 meters, where a hard pan was found presumably a clay layer. At the time of the seismic and GPR surveys, the water table was at a depth of 2.1 meters, based on a measurement in a test well located 25 meters from the seismic line. A well drilled about 40 meters away from the seismic line encountered bedrock (a fine- to medium-grained Cretaceous-age sandstone) at a depth of 29 meters.'

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.

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.