Ground Penetrating Radar Traveltime Research Articles

Coupled hydrogeophysical inversion of an artificial infiltration experiment monitored with ground-penetrating radar: synthetic demonstration

Abstract. In this study, we investigate the use of ground-penetrating radar (GPR) time-lapse monitoring of artificial soil infiltration experiments. The aim is to evaluate this protocol in the context of estimating the hydrodynamic unsaturated soil parameter values and their associated uncertainties. The originality of this work is to suggest a statistical parameter estimation approach using Markov chain Monte Carlo (MCMC) methods to have direct estimates of the parameter uncertainties. Using the GPR time data from the moving wetting front only does not provide reliable results. Thus, we propose to use additional information from other types of reflectors to optimize the quality of the parameter estimation. Water movement and electromagnetic wave propagation in the unsaturated zone are modeled using a one-dimensional hydrogeophysical model. The GPR travel time data are analyzed for different reflectors: a moving reflector (the infiltration wetting front) and three fixed reflectors located at different depths in the soil. Global sensitivity analysis (GSA) is employed to assess the influence of the saturated hydraulic conductivity Ks, the saturated and residual water contents θs and θr, and the Mualem–van Genuchten shape parameters α and n of the soil on the GPR travel time data of the reflectors. Statistical calibration of the soil parameters is then performed using the MCMC method. The impact of the type of reflector (moving or fixed) is then evaluated by analyzing the calibrated model parameters and their confidence intervals for different scenarios. GSA results show that the sensitivities of the GPR data to the hydrodynamic soil parameters are different between moving and fixed reflectors, whereas fixed reflectors at various depths have similar sensitivities. Ks has a similar and strong influence on the data of both types of reflectors. Concerning the other parameters, for the wetting front, only θs and α have an influence, and only at long time steps since the total variance is zero at the very beginning of the experiment. On the other hand, for the fixed reflectors, the total variance is not zero at the very start and the parameters θs, θr, α and n can have an influence from the very beginning of the infiltration. Results of parameter estimation show that the use of calibration data from the moving or fixed reflectors alone does not enable a good identification of all soil parameters. With the moving reflector, the error between the estimated mean value and the exact target value for θr and α is 9 % and 45 %, respectively, and less than 3 % for the other parameters. The best reduction of the size of the parameter distribution is obtained for n, with a posterior distribution 9 times smaller than the prior one. For the others, this reduction ratio varies between 1 and 5. For the fixed reflectors, the estimated mean values are far from the target values for α, θr and n, representing for a reflector located at 120 cm 15 %, 27 %, and 121 %, respectively. On the other hand, when both data are combined, all soil parameters can be well estimated with narrow confidence intervals. For instance, when using both data from the moving wetting front and a fixed reflector located at 120 cm for calibration, the estimated mean values of the errors of all parameters are less than 5 %. Moreover, all parameter distributions are well reduced, with a maximum reduction for Ks, leading to a posterior distribution being 46 times smaller than the prior one, and the worst but still satisfactory being for θr for which the posterior distribution is 8 times smaller than the prior one. The methodology was applied to fine, medium, and coarse sands with very good results, particularly for the finest soil. The thickness of the unsaturated zone was also tested (0.5, 1, and 2 m) and a better estimation of the hydrodynamic parameters is obtained when the water table is deeper. In addition, the height of water applied in the infiltrometry test influences the speed of the test without affecting the performance of the proposed method.

Coupled full-waveform inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic parameters: A synthetic study

Coupled inversion is a promising technique for determining soil hydraulic properties from time-lapse geophysical measurements. In this inversion approach, a hydrological model is coupled with ground penetrating radar (GPR) forward modelling to avoid interpretation errors from data processing. In this study, a workflow for coupled GPR full-waveform inversion (CFWI) is proposed that combines the benefits of coupled inversion and full-waveform inversion (FWI) to estimate soil hydraulic properties from time-lapse horizontal borehole GPR measurements. In particular, a synthetic modelling study is presented that compares the performance of coupled inversion of full waveforms and first arrival time data from zero-offset profile (ZOP) borehole measurements obtained during an infiltration event for estimating hydraulic parameters and the thickness of the first layer for a 2-layer soil profile. It was found that the thickness of the first layer could be more accurately estimated by CFWI because of the additional information contained in reflected and refracted waves from the air–soil layer boundary. Furthermore, the hydraulic parameters estimated by the coupled inversion of GPR travel times slightly deviated from true values and showed a relatively large uncertainty, whereas the results of CFWI precisely matched the known water retention and hydraulic conductivity functions. Despite these promising results, several challenges should be solved before CFWI can be applied to experimental GPR data. First, conducting CFWI needs accurate conceptual setups for the hydro(geo-)logical model. Furthermore, a robust approach should be developed to accurately estimate an effective source wavelet from the experimental ZOP data, and a more sophisticated hydrological modelling approach has to be considered to better estimate the distribution of soil electrical conductivity during the infiltration event. Finally, the accuracy and efficiency of current GPR modelling methods may need to be improved.

Approaching geoscientific inverse problems with vector-to-image domain transfer networks

We present vec2pix, a deep neural network designed to predict categorical or continuous 2D subsurface property fields from one-dimensional measurement data (e.g., time series), thereby offering an alternative approach to solve inverse problems. The performance of the method is investigated through two types of synthetic inverse problems: (a) a crosshole ground penetrating radar (GPR) tomography experiment with GPR travel times being used to infer a 2D velocity field, and (2) a multi-well pumping experiment within an unconfined aquifer with time series of transient hydraulic heads being used to retrieve a 2D hydraulic conductivity field. For each type of problem, both a multi-Gaussian and a binary channelized subsurface domain with long-range connectivity are considered. Using a training set of 20,000 examples (implying as many forward model evaluations), the method is found to recover a 2D model that is in much closer agreement with the true model than the closest training model in the forward-simulated data space. Further testing with smaller training sample sizes shows only a moderate reduction in performance when using 5000 training examples only. Although these 5000 to 20,000 forward runs can be performed offline in parallel, the associated computational expense may still be prohibitive for very demanding forward solvers. In addition, re-training is required for each new measurement configuration. Even if the recovered models are visually close to the true ones, the data misfits associated with their forward responses are generally larger than the noise level used to contaminate the true data. Uncertainty of the inverse solution is partially assessed using deep ensembles, in which the network is trained repeatedly with random initialization. Overall, this study advances understanding of how to use deep learning to infer subsurface models from indirect measurement data. More work is needed to evaluate the suitability of our proposed approach for real data, for which model errors and uncertainty in the geologic scenario will bring additional complexities.

Sequential and coupled inversion of horizontal borehole ground penetrating radar data to estimate soil hydraulic properties at the field scale

Horizontal borehole ground penetrating radar (GPR) measurements can provide valuable information on soil water content (SWC) dynamics in the vadose zone, and hence show potential to estimate soil hydraulic properties. In this study, the performance of both sequential and coupled inversion workflows to obtain soil hydraulic properties from time-lapse horizontal borehole GPR data obtained during an infiltration experiment were compared using a synthetic modelling study and the analysis of actual field data. The sequential inversion using the vadose zone flow model HYDRUS-1D directly relied on SWC profiles determined from the travel time of GPR direct waves using the straight-wave approximation. The synthetic modelling study showed that sequential inversion did not provide accurate estimates of the soil hydraulic parameters due to interpretation errors in the estimated SWC near the infiltration front and the ground surface. In contrast, the coupled inversion approach, which combined HYDRUS-1D with a forward model of GPR wave propagation (gprMax3D) and GPR travel time information, provided accurate estimates of the hydraulic properties in the synthetic modelling study. The application of the coupled inversion approach to measured borehole GPR data also resulted in plausible estimates of the soil hydraulic parameters. It was concluded that coupled inversion should be preferred over sequential inversion of time-lapse horizontal borehole GPR data in the presence of strong SWC gradients that occur during infiltration events.

Soil moisture estimation using tomographic ground penetrating radar in a MCMC\u2013Bayesian framework

In this study, we focus on a hydrogeological inverse problem specifically targeting monitoring soil moisture variations using tomographic ground penetrating radar (GPR) travel time data. Technical challenges exist in the inversion of GPR tomographic data for handling non-uniqueness, nonlinearity and high-dimensionality of unknowns. We have developed a new method for estimating soil moisture fields from crosshole GPR data. It uses a pilot-point method to provide a low-dimensional representation of the relative dielectric permittivity field of the soil, which is the primary object of inference: the field can be converted to soil moisture using a petrophysical model. We integrate a multi-chain Markov chain Monte Carlo (MCMC)–Bayesian inversion framework with the pilot point concept, a curved-ray GPR travel time model, and a sequential Gaussian simulation algorithm, for estimating the dielectric permittivity at pilot point locations distributed within the tomogram, as well as the corresponding geostatistical parameters (i.e., spatial correlation range). We infer the dielectric permittivity as a probability density function, thus capturing the uncertainty in the inference. The multi-chain MCMC enables addressing high-dimensional inverse problems as required in the inversion setup. The method is scalable in terms of number of chains and processors, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. The proposed inversion approach can successfully approximate the posterior density distributions of the pilot points, and capture the true values. The computational efficiency, accuracy, and convergence behaviors of the inversion approach were also systematically evaluated, by comparing the inversion results obtained with different levels of noises in the observations, increased observational data, as well as increased number of pilot points.

Traveltime tomography of crosshole ground-penetrating radar based on an arctangent functional with compactness constraints

Regularization has been an effective technique to provide unique and stable solutions for crosshole ground-penetrating radar (GPR) traveltime tomography. The traditional form of this method, in which a low-order differential operator was used, commonly yields a smooth solution that may not be appropriate when anomalies occur in block patterns, such as voids or irregular objects. The minimum support (MS) functional can be used to improve the resolution of blocky structures; however, in crosshole GPR traveltime tomography, the MS functional is unable to resolve residual artifacts, whose departure from an a priori model are smaller than the focusing parameter selected from a trade-off curve. In addition, it would result in severe instability and yield a trade-off curve with poorly defined corners when the focusing parameter nears the precision of the apparatus. We have developed a new stabilizing functional based on the arctangent (AT) function that effectively removes the artificially small values in the crosshole GPR traveltime tomography, and ultimately is more efficient because it does not require the user to select a focusing parameter. We inverted three 2D synthetic data sets based on the reweighted regularized conjugate gradient algorithm. Compared with the low-order differential and MS functional, the user will be able to clearly distinguish the anomaly boundary using this method, which will yield results that are closer to the actual structure. We also discussed the impact of some influencing factors caused by the noise contained in the data, the central frequency of the antenna, the anomalous trends, and the ray coverage angle. We further inverted an experimental data set to test the effectiveness and robustness of the method.

Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models

ABSTRACTVelocity models are essential to process two‐ and three‐dimensional ground‐penetrating radar (GPR) data. Furthermore, velocity information aids the interpretation of such data sets because velocity variations reflect important material properties such as water content. In many GPR applications, common midpoint (CMP) surveys are routinely collected to determine one‐dimensional velocity models at selected locations. To analyse CMP data gathers, spectral velocity analyses relying on the normal‐moveout (NMO) model are commonly employed. Using Dix’s formula, the derived NMO velocities can be further converted to interval velocities which are needed for processing and interpretation. Because of the inherent assumptions and limitations of such approaches, we investigate and propose an alternative procedure based on the global inversion of reflection travel‐times. We use a finite‐difference solver of the Eikonal equation to accurately solve the forward problem in combination with particle swarm optimization (PSO) to find one‐dimensional GPR velocity models explaining our data. Because PSO is a robust and efficient global optimization tool, our inversion approach includes generating an ensemble of representative solutions that allows us to analyse uncertainties in the model space. Using synthetic data examples, we test and evaluate our inversion approach to analyse CMP data collected across typical near‐surface environments. Application to a field data set recorded at a well‐constrained test site including a comparison to independent borehole and direct‐push data, further illustrates the potential of the proposed approach, which includes a straightforward and understandable appraisal of non‐uniqueness and uncertainty issues, respectively. We conclude that our methodology is a feasible and powerful tool to analyse GPR CMP data and allows practitioners and researchers to evaluate the reliability of CMP derived velocity models.

GPR utilization in artificial freezing engineering

To utilize ground penetrating radar (GPR) in artificial freezing engineering (AFE), the electromagnetic parameters (EMP) of frozen soil were measured using a vector network analyser, which showed that the dielectric permittivity and electric conductivity change abruptly at the boundary between the frozen and the non-frozen soil. Then similarity criteria of GPR model experiments were deduced, and GPR laboratory model experiments and field explorations of AFE were carried out. It was found that for AFE, the GPR travel time and profile characters of anomalies in model experiments were similar to those in field explorations, while the amplitude of GPR signals in laboratory model experiments were much stronger than those in field explorations. Numerical simulations were also implemented to analyse the relationship between model experiments and field explorations, which further told us why we could easily find the targets by GPR in the laboratory but not in field explorations. The outputs showed that GPR could be used to detect the thickness of the frozen wall and to find unfrozen soil defects, even though the amplitude of the reflective signals were much weaker than those of laboratory experiments. The research findings have an important theoretical value for AFE and permafrost region engineering, and the deduced GPR similarity criteria could be widely used in other GPR model experiments.

Examining the information content of time-lapse crosshole GPR data collected under different infiltration conditions to estimate unsaturated soil hydraulic properties

Time-lapse geophysical data acquired during transient hydrological experiments are being increasingly employed to estimate subsurface hydraulic properties at the field scale. In particular, crosshole ground-penetrating radar (GPR) data, collected while water infiltrates into the subsurface either by natural or artificial means, have been demonstrated in a number of studies to contain valuable information concerning the hydraulic properties of the unsaturated zone. Previous work in this domain has considered a variety of infiltration conditions and different amounts of time-lapse GPR data in the estimation procedure. However, the particular benefits and drawbacks of these different strategies as well as the impact of a variety of key and common assumptions remain unclear. Using a Bayesian Markov-chain-Monte-Carlo stochastic inversion methodology, we examine in this paper the information content of time-lapse zero-offset-profile (ZOP) GPR traveltime data, collected under three different infiltration conditions, for the estimation of van Genuchten–Mualem (VGM) parameters in a layered subsurface medium. Specifically, we systematically analyze synthetic and field GPR data acquired under natural loading and two rates of forced infiltration, and we consider the value of incorporating different amounts of time-lapse measurements into the estimation procedure. Our results confirm that, for all infiltration scenarios considered, the ZOP GPR traveltime data contain important information about subsurface hydraulic properties as a function of depth, with forced infiltration offering the greatest potential for VGM parameter refinement because of the higher stressing of the hydrological system. Considering greater amounts of time-lapse data in the inversion procedure is also found to help refine VGM parameter estimates. Quite importantly, however, inconsistencies observed in the field results point to the strong possibility that posterior uncertainties are being influenced by model structural errors, which in turn underlines the fundamental importance of a systematic analysis of such errors in future related studies.

Bayesian Markov‐Chain‐Monte‐Carlo Inversion of Time‐Lapse Crosshole GPR Data to Characterize the Vadose Zone at the Arrenaes Site, Denmark

The ground‐penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten–Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady‐state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time‐lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov‐chain‐Monte‐Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time‐lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time‐lapse GPR data for characterizing vadose zone dynamics.

SVD Inversion of Zero-Offset Profiling Data Obtained in the Vadose Zone Using Cross-Borehole Radar

An artificial groundwater infiltration experiment was conducted in Nagaoka city in Japan, and time-lapse cross-borehole ground-penetrating radar (GPR) data were collected to monitor the infiltration process in the vadose zone using zero-offset profiling (ZOP) mode. The downward migration of induced water was observed as a variation of GPR travel times, which can be transformed into dielectric constant and further converted to volumetric water content. In this paper, we present an effective approach to extract accurate information about the hydrogeologic process in the vadose zone from ZOP data. This approach is based on a least squares inversion method using singular-value decomposition, in which a finite-difference time-domain forward modeling is used for computing electromagnetic wave fields on 2-D cylindrical coordinates. The inversion approach is validated using a synthetic example and applied to the field data. We can successfully estimate the variation of soil water content during infiltration in the Nagaoka site from the reconstructed dielectric constant models. The inversion results show that the saturation information is useful to assess hydrogeologic properties of the test soil zone.

Efficient Electromagnetic Imaging of an Artificial Infiltration Process in the Vadose Zone Using Cross-Borehole Radar

Cross-borehole ground-penetrating radar (GPR) has been widely used to characterize the shallow subsurface and to monitor hydrogeologic processes. To investigate an infiltration process in the vadose zone, an artificial groundwater infiltration test was conducted in Nagaoka, Japan. Time-lapse cross-borehole GPR data were collected using zero-offset profiling (ZOP) mode. The infiltration process was observed as a variation of GPR traveltimes, which can be transformed into a dielectric constant, and further converted to volumetric water content. A standard ZOP analysis, for which all first arrivals are assumed to be direct waves, results in an underestimation of the dielectric constant because of the existence of critically refracted waves. This letter presents an efficient algorithm using the maximum first-cycle amplitude to approximately determine the traveltime of direct arrival, deriving a dielectric constant model more accurately than the standard ZOP analysis from ZOP data. Tests on synthetic and real field data show that the proposed approach is effective in building accurate water content profile without iterative calculations as in the standard ZOP analysis.

Structural joint inversion of time‐lapse crosshole ERT and GPR traveltime data

Time‐lapse geophysical monitoring and inversion are valuable tools in hydrogeology for monitoring changes in the subsurface due to natural and forced (tracer) dynamics. However, the resulting models may suffer from insufficient resolution, which leads to underestimated variability and poor mass recovery. Structural joint inversion using cross‐gradient constraints can provide higher‐resolution models compared with individual inversions and we present the first application to time‐lapse data. The results from a synthetic and field vadose zone water tracer injection experiment show that joint 3‐D time‐lapse inversion of crosshole electrical resistance tomography (ERT) and ground penetrating radar (GPR) traveltime data significantly improve the imaged characteristics of the point injected plume, such as lateral spreading and center of mass, as well as the overall consistency between models. The joint inversion method appears to work well for cases when one hydrological state variable (in this case moisture content) controls the time‐lapse response of both geophysical methods.

Geostatistical inference using crosshole ground-penetrating radar

High-resolution tomographic images obtained from crosshole geophysical measurements have the potential to provide valuable information about the geostatistical properties of unsaturated-zone hydrologic-state variables such as moisture content. Under drained or quasi-steady-state conditions, the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole ground-penetrating-radar (GPR) traveltimes result in smooth, minimum-variance estimates of the subsurface radar wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding on a previous study, we have determined that it is possible to obtain estimates of global variance andmean velocity values of the subsurface as well as the correlation lengths describing the subsurface velocity structures. Accurate estimation of the global variance is crucial if stochastic realizations of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately [Formula: see text] apart, an observation confirmed by a GPR reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisture-content measurements, obtained gravimetrically from samples collected at the field site.

Inverse modeling of unsaturated flow parameters using dynamic geological structure conditioned by GPR tomography

A method is presented to estimate flow parameters and geological structure in the vadose zone by combining time‐lapse Ground Penetrating Radar (GPR) traveltime tomography and inverse flow modeling. The traveltime tomography is used to determine the spatial electromagnetic velocity distribution in the vadose zone. These time‐lapse velocity images are converted to time‐lapse volumetric soil water content images using petrophysical relationships. Subsequently, the water content images are used as constraints in the flow inversion. The influence of the tomographic artifacts on the flow inversion is minimized by assigning weights that are proportional to the ray coverage. Our flow inversion algorithm estimates the flow parameters and calibrates the geological structure. The geological structure is defined using a set of control points, the positions of which can be modified during the inversion. After the inversion, the final geological and flow model are used to compute GPR traveltimes to check the consistency between these computed traveltimes and the observed traveltimes.The method is first tested on two synthetic models (a steady state and a transient flow models). Subsequently, the method is applied to characterize a real vadose zone at Oslo Airport Gardermoen, Norway, during the snowmelt in 2005. The flow inversion method is applied to locate and quantify the main geological layers at the site. In particular the inversion method identifies and estimates the location and properties of thin dipping layers with relatively low‐permeability. The flow model is cross validated using an independent infiltration event.

Target-oriented interferometric tomography for GPR data

An interferometric form of Fermat’s principle and traveltime tomography is used to invert ground-penetrating radar (GPR) data for the subsurface velocity distribution. The input data consist of GPR traveltimes of reflections from two buried interfaces, [Formula: see text] (reference) and [Formula: see text] (target), where the data are excited and recorded by GPR antennas at the surface. Fermat’s interferometric principle is then used to redatum the surface transmitters and receivers to interface [Formula: see text] so the associated reflection traveltimes correspond to localized transit times between interfaces [Formula: see text] and [Formula: see text]. The overburden velocity model above interface [Formula: see text] is not required. The result after tomographic inversion is a high-resolution estimate of the velocity between interfaces [Formula: see text] and [Formula: see text] that does not depend on the velocity model above interface [Formula: see text]. A motivation for introducing interferometric traveltime tomography is that typical layer-stripping approaches will see the slowness error increase with depth as the layers are inverted. This suggests that near-surface statics errors are propagated and amplified with depth. In contrast, the interferometric traveltime tomography method largely eliminates statics errors by taking the difference between reflection events that emanate from neighboring layer interfaces. Slowness errors are not amplified with depth. However, the method is sensitive to the estimation accuracy for the geometry of the reference interface. Both synthetic and real field data are used successfully to validate the effectiveness of this interferometric technique.

Indirect diagnosis of pavement structural damages using surface GPR reflection techniques

The safety and operability of road networks is, in part, dependent on the quality of the pavement. It is known that pavements suffer from many different structural problems which can lead to damage to the pavement surface. To minimize the effect of these problems programmed policies for pavement management are required. Additionally a given local anomaly on the road surface can affect the safety of the road to various degrees according to the category of the road, so it is possible to set up different programmes of repair according to the different standards of road. Programmed policies for pavement management are required because of the wide structural damage which occurs to pavements during their normal operating life. This has consequences for the safety and operability of road networks. During the last decade, road networks suffered from great structural damage. The damage occurs for different reasons, such as the increasing traffic or the lack of means for routine maintenance. Many forms of damage, originating in the bottom layers are invisible until the pavement cracks. They depend on the infiltration of water and the presence of cohesive soil greatly reduces the bearing capacity of the sub-asphalt layers and underlying soils. On the basis of an in-depth literature review, an experimental survey with Ground Penetrating Radar (GPR) was carried out to calibrate the geophysical parameters and to validate the reliability of an indirect diagnostic method of pavement damage. The experiments were set on a pavement under which water was injected over a period of several hours. GPR travel time data were used to estimate the dielectric constant and the water content in the unbound aggregate layer, the variations in water content with time and particular areas where rate of infiltration decreases. A new methodology has been proposed to extract the hydraulic permittivity fields in sub-asphalt structural layers and soils from the moisture maps observed with GPR. It is effective at diagnosing the presence of clay or cohesive soil that compromises the bearing capacity of sub-base and induces damage.

Assessment of fractures in some columns inside the crypt of the Cattedrale di Otranto using integrated geophysical methods

We investigate the problem of internal fracturing of the columns of a building of cultural importance (the Crypt of the “Cattedrale di Otranto”, Apulia, Italy), using a multi-disciplinary approach, integrating different non-destructive techniques (Microclimatic and Geophysical). In fact historic buildings, over centuries, may accumulate a high concentration of water and/or moisture that increases significantly the deterioration rate, diminishing the mechanical resistance. It is well known that the distribution of moisture within stone strongly depends on ambient conditions, i.e. temperature and relative humidity. No suitable environmental condition in the Crypt and the presence of wet buried structures in the ground involved in the process of deterioration have been indeed assessed in previous surveys. Actually we perform a ground penetrating radar (GPR) investigation, in reflection mode, in order to reveal the existence of fractures inside the columns, because of the important rising of water from pavement. The reconstruction of the fracture distribution is fundamental to plan the safeguard and the restoration of the columns themselves and also the stability of the Crypt and of the uppermost Cathedral. Such survey was performed on seven columns using GPR technique. Moreover the electrical resistivity tomography (ERT) and GPR traveltime tomography (TT) methods were applied on the most damaged column. The results obtained with these different methodologies are in good agreement, however, the 2D electrical resistivity tomography seems to be less resolutive than GPR. While the GPR traveltime tomography is more time expensive than GPR reflection mode method. The GPR method, in reflection mode, allows to reconstruct the main fractures inside the columns also in virtue of its penetration power. We think that the joining of different methodologies permits to better understand the complex physical mechanisms in the phenomenon of deterioration of the cultural heritage, in order to achieve useful information for possible, future, restoration works.

Random and Non-Random Uncertainties in Precision GPR Measurements: Identifying and Compensating for Instrument Drift

The precision of ground penetrating radar (GPR) data is of increasing interest, as the technology finds ever increasing applications to near surface geophysical studies. Our group has undertaken a series of studies to identify the precision and accuracy with which GPR traveltimes, velocities and interval properties can be estimated under controlled conditions, recently reporting that random errors in two-way traveltime and velocity are on the order of ±0.7 ns and ±0.001 m/ns, respectively, at the 95% confidence level. The high degree of precision in that dataset makes it possible to observe non-random patterns in the independent estimates of the same parameter, suggesting possible systematic biases in the data. One source of systematic error that workers often encounter is the effect of back-scattering from above ground features. Another that we documented in our previous study is that the material properties of the subsurface may change from one survey to the next. A third possibility is non-random instrumentation error, one source of which, and the focus of the present report, is a variation in the time base. We address ways to identify its presence, to assess its influence on estimating GPR parameters, and how and when to compensate for its effects. Using a static transmitter (Tx) and receiver (Rx) offset to record a series of GPR traces over a nominal period of one hour, we find that time base drift can be significant for up to 25 min after turning on the instrument. Fortunately, the type of drift that we have found to be most apparent—time zero drift—can be readily identified and compensated for, if one employs the air phase at a number of Tx–Rx offsets. An invariant condition in a GPR survey is that the true velocity of the direct air phase should be the velocity of an electromagnetic pulse in free space. Thus, upon carefully determining the observed velocity of the air phase, and using the ratio between it and the true value of 0.29979 m/ns, one can correct the entire time base of a radargram. Guidelines for when to apply the air phase compensation technique are based on whether the observed velocity of the air phase falls outside the limits of an acceptable precision under the most ideal circumstances. We illustrate the compensation procedure using two CMPs collected on different days at the same survey position. Analyzing the uncompensated data unsurprisingly yields two different subsurface velocities, however the depth estimates of the same subsurface reflector differ by 0.5 m, which for this site is physically unlikely. The difference between the observed and the true air phase velocities for both data sets exceeded the minimum expected error according to our guidelines, thus after applying our time base correction, the difference between the depth estimates improves to 0.05~m.

Field‐scale estimation of volumetric water content using ground‐penetrating radar ground wave techniques

Ground‐penetrating radar (GPR) ground wave techniques were applied to estimate soil water content in the uppermost ∼10 cm of a 3 acre California vineyard several times over 1 year. We collected densely spaced GPR travel time measurements using 900 and 450 MHz antennas and analyzed these data to estimate water content. The spatial distribution of water content across the vineyard did not change significantly with time, although the absolute water content values varied seasonally and with irrigation. The GPR estimates of water content were compared to gravimetric water content, time domain reflectometry, and soil texture measurements. The comparisons of GPR‐derived estimates of water content to gravimetric water content measurements showed that the GPR estimates had a root mean square error of volumetric water content of the order of 0.01. The results from this study indicate that GPR ground waves can be used to provide noninvasive, spatially dense estimates of shallow water content over large areas and in a rapid manner.