Crosshole Ground-penetrating Radar Research Articles

Monte Carlo Full-Waveform Inversion of Cross-Hole Ground-Penetrating Radar Data Based on Improved Residual Network

A full-waveform inversion (FWI) of ground-penetrating radar (GPR) data can be used to effectively obtain the parameters of a shallow subsurface. Introducing the Markov chain Monte Carlo (MCMC) algorithm into the FWI can reduce the dependence on the initial model and obtain the global optimal solution, but it requires a large number of computations. In order to better detect underground targets based on ground-penetrating radar data, this paper proposes a joint scheme of an improved ResNet and an MCMC full-waveform inversion. This scheme combines the Bayesian MCMC algorithm and an improved ResNet to accurately invert the target dielectric permittivity. The introduction of deep learning networks into the forward calculation part of the MCMC inversion algorithm replaced the complex forward simulation process, greatly improving the inversion speed. It is worth noting that the neural network model was an approximation of complex forward modeling, and, therefore, it contained modeling errors. The MCMC method could quantify and explain modeling errors during the inversion process, reducing the impact of modeling errors on the inversion results. Finally, a simulation dataset was constructed for training and testing, and the errors were statistically analyzed. The results showed that this method can accurately reconstruct underground medium targets, and it has strong robustness and efficiency.

Linking horizontal crosshole GPR variability with root image information for maize crops

AbstractNon‐invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in‐situ time‐lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the “trend‐corrected spatial permittivity deviation of vegetated field,” allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non‐root‐related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend‐corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross‐correlated with the root volume fraction, resulting in a coefficient of determination (R2) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability.

Multi-year belowground data of minirhizotron facilities in Selhausen

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods.

GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures

The crosshole ground-penetrating radar (GPR) technique is widely used to characterize subsurface structures, yet the interpretation of crosshole GPR data involves solving non-linear and ill-posed inverse problems. In this work, we developed a generative adversarial network (GAN)-based inversion framework to translate crosshole GPR images to their corresponding 2D defect reconstruction images automatically. This approach uses fully connected layers to extract global features from crosshole GPR images and employs a series of cascaded U-Net structures to produce high-resolution defect reconstruction results. The feasibility of the proposed framework was demonstrated on a synthetic crosshole GPR dataset created with the finite-difference time-domain (FDTD) method and real-world data from a field experiment. Our inversion network obtained recognition accuracy of 91.36%, structural similarity index measure (SSIM) of 0.93, and RAscore of 91.77 on the test dataset. Furthermore, comparisons with ray-based tomography and full-waveform inversion (FWI) suggest that the proposed method provides a good balance between inversion accuracy and efficiency and has the best generalization when inverting actual measured crosshole GPR data.

High‐Resolution Geological Information from Crosshole Ground Penetrating Radar in Clayey Tills

AbstractHeterogeneous glacial deposits dominate large parts of the Northern Hemisphere. In these landscapes, high‐resolution characterization of the geology is crucial for understanding contaminant transport. Geological information is mostly obtained from multiple boreholes drilled during a site investigation, but such point‐based data alone do not always provide the required resolution to map small‐scale heterogeneity between boreholes. Crosshole ground penetrating radar (GPR) is suggested as a tool for adding credible geological information between boreholes at contaminated site investigations in industrial sites where infrastructure, such as electrical installations, can pose a challenge to other geophysical methods. GPR data are sensitive to the dielectric permittivity and the bulk electrical conductivity, which can be related to the distribution of water content and sand/clay occurrences. Here we present a detailed crosshole GPR dataset collected at an industrial contaminated site in a clay till setting. The data are processed using a novel inversion approach where information on changes in the velocity and attenuation of the radar signal are obtained independently. The GPR results are compared to borehole logs, grain size analyses, and relative permeability data from the site. The GPR data analysis provided valuable information on the understanding of the lateral geological variability. A silt layer with a thickness of a few decimeters, likely important for flow characterization, was confirmed and resolved by GPR data. Our findings suggest that crosshole GPR has the potential for contributing with high‐resolution geological information by filling the data gap between boreholes, thereby becoming a relevant tool in contaminated site investigations.

Prediction of porosity and water saturation of chalks from combined refraction seismic and reflection ground-penetrating radar measurements

The prediction of porosity and water content of near-surface chalks is important for agricultural, environmental, hydrologic, and geologic engineering investigations. However, current methods based on laboratory measurements or crosshole ground-penetrating radar (GPR) are typically costly and applicable to only a limited area. We have combined the refraction seismic and reflection GPR data sets measured on the ground surface along a 96 m long profile line to predict the porosity and water saturation simultaneously. Using integrated rock-physics templates, we develop and apply a quantitative method that combines acoustic and electromagnetic velocities to estimate the petrophysical parameters of chalks. The seismic and GPR data are processed independently and subsequently combined to invert the porosity and water saturation. Field data tests demonstrate that the predicted results are consistent with laboratory measurements and field observations, demonstrating that the integration of seismic and GPR data facilitates efficient and reliable estimates of the porosity and saturation of chalks on scales relevant to regional field mapping. Hence, this method allows the petrophysical parameters of near-surface chalks to be evaluated efficiently at a regional scale.

Crosshole Ground‐Penetrating Radar in Clay‐Rich Quaternary Deposits: Toward Characterization of High‐Loss Media

AbstractKnowing the centimeter‐to meter‐scale distribution of sand in clayey deposits is important for determining the dominating water flow pathways. Borehole information has a high vertical resolution, on the millimeter‐ to centimeter‐scale, but provides poor lateral coverage. For highly heterogeneous deposits, such as glacial diamicts, this detailed borehole information may not be sufficient for creating reliable geological models. Crosshole ground‐penetrating radar (GPR) can provide information on the decimeter‐ to meter‐scale variation between boreholes, but has mainly been used in sandy environments. In this study, we investigate whether crosshole GPR can provide information on the structural relationship of diamicts and their material properties, such as water content, bulk density, and clay content. To achieve ground truth, we compare the crosshole GPR data with geological information from boreholes and an excavation at the field site. The GPR data was analyzed comprehensively using several radar wave attributes in both time‐ and frequency domain, describing the signal velocity, strength, and shape. We found small variations in signal velocity (between 0.06 and 0.07 m/ns) but large variations in both amplitude and shape (either order of magnitude variation or doubling/tripling of attribute values). We see that the GPR response from wetter and more clayey diamicts have both lower amplitudes and lower centroid frequencies than the response from their drier and sandier counterparts. Lastly, we find that the variation in amplitude and shape attributes is better correlated to the diamicts' material properties than the signal velocity.

Accounting for Modeling Errors in Linear Inversion of Crosshole Ground‐Penetrating Radar Amplitude Data: Detecting Sand in Clayey Till

AbstractMapping high permeability sand occurrences in clayey till is fundamental for protecting the underlying drinking water resources. Crosshole ground penetrating radar (GPR) amplitude data have the potential to differentiate between sand and clay, and can provide 2D subsurface models with a decimeter‐scale resolution. We develop a probabilistic straight‐ray‐based inversion scheme, where we account for the forward modeling error arising from choosing a straight‐ray forward solver. The forward modeling error is described by a Gaussian probability distribution and included in the total noise model by addition of covariance models. Due to the linear formulation, we are able to decouple the inversion of traveltime and amplitude data and obtain results fast. We evaluate the approach through a synthetic study, where synthetic traveltime and amplitude data are inverted to obtain slowness and attenuation tomograms using several noise model scenarios. We find that accounting for the forward modeling error is fundamental to successfully obtain tomograms without artifacts. This is especially the case for inversion of amplitude data since the structure of the noise model for the forward modeling error is significantly different from the other data error models. Overall, inversion of field data confirms the results from the synthetic study; however, amplitude inversion performs slightly better than traveltime inversion. We are able to characterize a 0.4–0.6 m thick sand layer as well as internal variations in the clayey till matching observed geological information from borehole logs and excavation.

Detection of Tracer Plumes Using Full‐Waveform Inversion of Time‐Lapse Ground Penetrating Radar Data: A Numerical Study in a High‐Resolution Aquifer Model

AbstractImaging 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. The method produces images of both relative dielectric permittivity (εr) and bulk electrical conductivity (σb) that can be related to hydraulic aquifer properties and tracer distributions. To test the potential of time‐lapse GPR FWI for imaging tracer plumes, we conducted a numerical tracer experiment by injecting saline water, desalinated water, and ethanol in a heterogeneous aquifer. The saline and desalinated tracers only changed σb, whereas ethanol changed both εr and σb. Tracer concentrations were retrieved from the inverted εr and σb models using information about petrophysical parameters. GPR FWI εr and σb tracer images could recover preferential paths of ∼0.2 m width, while the derived σb structures are smoother. FWI of 50 time‐lapse data sets demonstrated the potential of the FWI to derive spatially resolved breakthrough curves of the saline and ethanol tracer in the image plane between the boreholes. Thus, high‐resolution imaging with GPR FWI of tracers that produce a high permittivity contrast against the background has a great potential for characterization of heterogeneous transport in aquifers.

3-D Electromagnetic Modeling Explains Apparent-Velocity Increase in Crosshole GPR Data-Borehole Fluid Effect Correction Method Enables to Incorporating High-Angle Traveltime Data

For high-resolution crosshole ground-penetrating radar (GPR) tomography, a wide-range of ray path angles are required, including transmitter-receiver pairs with high-angles. However, artifacts have been observed in the inverted GPR tomograms when high-angle data were incorporated in ray-based inversion (RBI) tomography due to not well-understood increasing apparent velocities for increasing ray-angles. To reduce these artifacts, it is common practice to limit the angular aperture to a threshold between 30° and 50°, which reduces the spatial resolution. We apply 3-D finite-difference time-domain GPR modeling including borehole fluid and resistive loaded finite-length antenna (FLA) models to study the increase of apparent velocity with increasing ray path angle. This study shows that the strong refraction of the electromagnetic waves at the borehole interface between water and subsurface is one of the reasons for these not well-understood phenomena. We introduce a novel borehole-fluid effect correction (BFEC) that relocates the transmitter and receiver positions to the location, where the refraction is occurring to remove any influence of the borehole such that the remaining traveltimes can be inverted using an RBI. BFEC improves the estimated apparent-velocity (relative permittivity) values and enables the incorporation of wide-angle ray paths resulting in more accurate tomograms. We verify the BFEC with a simple layered subsurface model and a realistic synthetic model. By applying curved-ray RBI without and with the BFEC, the subsurface structures are reconstructed with more details for the BFEC data and the average relative error model reduced from 13% to below 8% for the high-resolution inhomogeneous model.

Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method

Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse GPR full-waveform data to invert the dielectric permittivity. An inversion based on the MCMC method does not rely on an accurate initial model and can introduce any complex prior information. Time-lapse ground-penetrating radar has great potential to monitor the properties of a subsurface. For the time-lapse inversion, we used the double difference method to invert the time-lapse target area accurately and full-waveform data. We propose a local sampling strategy taking advantage of the a priori information in the Monte Carlo method, which can sample only the target area with a sequential Gibbs sampler. This method reduces the calculation and improves the inversion accuracy of the target area. We have provided inversion results of the synthetic time-lapse waveform data that show that the proposed method significantly improves accuracy in the target area.

Lithological tomography with the correlated pseudo-marginal method

SUMMARYWe consider lithological tomography in which the posterior distribution of (hydro)geological parameters of interest is inferred from geophysical data by treating the intermediate geophysical properties as latent variables. In such a latent variable model, one needs to estimate the intractable likelihood of the (hydro)geological parameters given the geophysical data. The pseudo-marginal (PM) method is an adaptation of the Metropolis–Hastings algorithm in which an unbiased approximation of this likelihood is obtained by Monte Carlo averaging over samples from, in this setting, the noisy petrophysical relationship linking (hydro)geological and geophysical properties. To make the method practical in data-rich geophysical settings with low noise levels, we demonstrate that the Monte Carlo sampling must rely on importance sampling distributions that well approximate the posterior distribution of petrophysical scatter around the sampled (hydro)geological parameter field. To achieve a suitable acceptance rate, we rely both on (1) the correlated PM (CPM) method, which correlates the samples used in the proposed and current states of the Markov chain and (2) a model proposal scheme that preserves the prior distribution. As a synthetic test example, we infer porosity fields using crosshole ground-penetrating radar (GPR) first-arrival traveltimes. We use a (50 × 50)-dimensional pixel-based parametrization of the multi-Gaussian porosity field with known statistical parameters, resulting in a parameter space of high dimension. We demonstrate that the CPM method with our proposed importance sampling and prior-preserving proposal scheme outperforms current state-of-the-art methods in both linear and non-linear settings by greatly enhancing the posterior exploration.

Practical data acquisition strategy for time-lapse experiments using crosshole GPR and full-waveform inversion

Crosshole ground penetrating radar (GPR) methods are increasingly used in time-lapse studies of flow in the uppermost near subsurface with important implications for our understanding of e.g., water infiltration in the unsaturated zone, and fluid flow in the saturated zone. A particular challenge in such time-lapse crosshole studies is the trade-off between collecting sufficient data to be able to resolve how a tracer moves, and, minimizing the data acquisition time such that the data approximates a static state. We test how dense recording geometries are needed for resolving a gas bubble injected in a highly heterogeneous chalk reservoir analogue using a full-waveform inversion (FWI) approach for modelling the crosshole GPR data. We show that even relatively sparse geometries provide sufficient resolution of the permittivity contrast caused by the gas bubble, provided that the detailed background permittivity structure is known from prior (before gas injection) FWI analysis of densely recorded high-resolution data. The conductivity contrast caused by the gas is more challenging to recover and the resolution suffers to a higher degree when reducing the survey geometry or at higher noise levels. As long as the permittivity change during the time-lapse experiment is the main target, a significant reduction in acquisition time is therefore possible as compared to the time needed to record the background permittivity structure. This reduced acquisition time has important practical implications for time-lapse experiments under realistic conditions. Our results are based on synthetic analysis based on a realistic subsurface scenario closely linked to characterization of heterogeneous chalk reservoirs. However, our findings also have important implications for planning of future time-lapse studies in other settings.

Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors

SUMMARY Bayesian model selection enables comparison and ranking of conceptual subsurface models described by spatial prior models, according to the support provided by available geophysical data. Deep generative neural networks can efficiently encode such complex spatial priors, thereby, allowing for a strong model dimensionality reduction that comes at the price of enhanced non-linearity. In this setting, we explore a recent adaptive sequential Monte Carlo (ASMC) approach that builds on annealed importance sampling (AIS); a method that provides both the posterior probability density function (PDF) and the evidence (a central quantity for Bayesian model selection) through a particle approximation. Both techniques are well suited to parallel computation and rely on importance sampling over a sequence of intermediate distributions, linking the prior and the posterior PDF. Each subsequent distribution is approximated by updating the particle weights and states, compared with the previous approximation, using a small pre-defined number of Markov chain Monte Carlo (MCMC) proposal steps. Compared with AIS, the ASMC method adaptively tunes the tempering between neighboring distributions and performs resampling of particles when the variance of the particle weights becomes too large. We evaluate ASMC using two different conceptual models and associated synthetic cross-hole ground penetrating radar tomography data. For the most challenging test case, we find that the ASMC method is faster and more reliable in locating the posterior PDF than state-of-the-art adaptive MCMC. The evidence estimates are found to be robust with respect to the choice of ASMC algorithmic variables and much less sensitive to the model proposal type than MCMC. The variance of the evidence estimates are best estimated by replication of ASMC runs, while approximations based on single runs provide comparable estimates when using a sufficient number of proposal steps in approximating each intermediate distribution.

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.

Improvement of ground-penetrating radar full-waveform inversion images using cone penetration test data

Detailed characterization of aquifers is critical and challenging due to the existence of heterogeneous small-scale high-contrast layers. For an improved characterization of subsurface hydrologic characteristics, crosshole ground-penetrating radar (GPR) and cone penetration test (CPT) measurements are performed. In comparison to the CPT approach, which can only provide 1D high-resolution data along vertical profiles, crosshole GPR enables measuring 2D cross sections between two boreholes. In general, a standard inversion method for GPR data is the ray-based approach, which considers only a small amount of information and can therefore only provide limited resolution. In the past few decades, full-waveform inversion (FWI) of crosshole GPR data in the time domain has matured, and it provides inversion results with higher resolution by exploiting the full-recorded waveform information. However, FWI results are limited due to complex underground structures and the nonlinear nature of the method. A new approach that uses CPT data in the inversion process is applied to enhance the resolution of the final relative permittivity FWI results by updating the effective source wavelet. The updated effective source wavelet possesses a priori CPT information and a larger bandwidth. Using the same starting models, a synthetic model comparison between the conventional and updated FWI results demonstrates that the updated FWI method provides reliable and more consistent structures. To test the method, five experimental GPR cross section results are analyzed with the standard FWI and the new proposed updated approach. The synthetic and experimental results indicate the potential of improving the reconstruction of subsurface aquifer structures by combining conventional 2D FWI results and 1D CPT data.

Multi-scale phase waveform inversion method of on-ground GPR data

Full waveform inversion of ground penetrating radar (GPR) based on Maxwell’s equations can invert the permittivity and conductivity of the underground medium, and achieve quantitative interpretation of the target formation. Here, we present a GPR waveform inversion method based on the phase misfit function, and inverts the phase information in the GPR data. By analyzing the characteristics of phase transformation, the misfit function of PWI is given. The gradient is constructed by the convolution of the forward and reverse fields, and its gradient formula is derived. When the transmitting source and receiver of GPR are arranged on the ground, the waveform inversion can only use the scattered field to obtain the effective information of the underground medium, and the inversion will face a strong indetermination. The inversion is more dependent on the initial model than the cross-hole GPR waveform inversion, but non-destructive detection often lacks prior information. In order to solve this problem, this paper makes use of the characteristics of integral operation and formulates a multi-scale inversion strategy. In the iterative process of inversion, high integration times are preferentially used to restore long wavelength information. This can reduce the dependence of the waveform inversion on the initial model as much as possible, implement the waveform inversion method for on-ground GPR data, and verify it with two sets of synthetic data.

Analysis of Forward Model, Data Type, and Prior Information in Probabilistic Inversion of Crosshole GPR Data

The crosshole ground penetrating radar (GPR) is a widely used tool to map subsurface properties, and inversion methods are used to derive electrical parameters from crosshole GPR data. In this paper, a probabilistic inversion algorithm that uses Markov chain Monte Carlo (MCMC) simulations within the Bayesian framework is implemented to infer the posterior distribution of the relative permittivity of the subsurface medium. Close attention is paid to the critical elements of this method, including the forward model, data type and prior information, and their influence on the inversion results are investigated. First, a uniform prior distribution is used to reflect the lack of prior knowledge of model parameters, and inversions are performed using the straight-ray model with first-arrival traveltime data, the finite-difference time-domain (FDTD) model with first-arrival traveltime data, and the FDTD model with waveform data, respectively. The cases using first-arrival traveltime data require an unreasonable number of model evaluations to converge, yet are not able to recover the real relative permittivity field. In contrast, the inversion using the FDTD model with waveform data successfully infers the correct model parameters. Then, the smooth constraint of model parameters is employed as the prior distribution. The inversion results demonstrate that the prior information barely affects the inversion results using the FDTD model with waveform data, but significantly improves the inversion results using first-arrival traveltime data by decreasing the computing time and reducing uncertainties of the posterior distribution of model parameters.

3D aquifer characterization of the Hermalle-sous-Argenteau test site using crosshole ground-penetrating radar amplitude analysis and full-waveform inversion

To improve the understanding of flow and transport processes in the critical zone, high-resolution and accurate estimation of the small-scale heterogeneity is essential. Preferential flow paths related to high-porosity layers and clay lenses in gravel aquifers greatly affect flow and transport processes in the subsurface, and their high electrical contrast to their surrounding matrix and limited extent can act as low-velocity electromagnetic waveguides. In the past decade, time-domain full-waveform inversion (FWI) of crosshole ground-penetrating radar (GPR) data has shown to provide 2D decimeter-scale resolution images of relative permittivity and electrical conductivity of the subsurface, which can be related to porosity and soil texture. Most studies using crosshole GPR FWI resolved high-porosity zones that were identified by an amplitude analysis approach. But clay lenses or zones with higher electrical conductivity that act as low-velocity waveguides are hard to distinguish in the measured data and amplitude analysis because of the absence of characteristic wave-propagation features. We have investigated a set of nine crosshole GPR data sets from a test site in Hermalle-sous-Argenteau near the Meuse River in Belgium to characterize the aquifer within a decimeter-scale resolution and to improve the understanding of a previously performed heat tracer experiment. Thereby, we extend the amplitude analysis to identify two different types of low-velocity waveguides either caused by an increased porosity or a higher electrical conductivity (and higher porosity). Combining the GPR amplitude analysis for low-velocity waveguide zones with the standard FWI results provided information on waveguide zones, which modified the starting models and further improved the FWI results. Moreover, an updated effective source wavelet is estimated based on the updated permittivity starting models. In comparison with the traditional FWI results, the updated FWI results present smaller gradient of the medium properties and smaller root-mean-squared error values in the final inversion results. The nine crosshole sections are used to generate a 3D image of the aquifer and allowed a detailed analysis of the porosity distribution along the different sections. Consistent structures of the permittivity and electrical conductivity show the robustness of the updated FWI results. The aquifer structures obtained by the FWI results agree with those results of the heat tracer experiment.

Two-dimensional Time-domain Full Waveform Inversion of On-ground Common-offset GPR Data Based on Integral Preprocessing

Full waveform inversion (FWI) is an advanced inversion technique for ground penetrating radar (GPR), which could provide quantitative, high-resolution subsurface imaging. FWI has been used widely to process crosshole and on-ground multi-offset GPR data, but its application to on-ground common-offset GPR data is more difficult and being developed. This is mainly because that on-ground common-offset GPR has much less coverage of the subsurface and mainly includes reflective information. The application of conventional FWI to pure reflection data in the absence of a highly accurate starting velocity model is difficult. Here, we demonstrate a means of achieving this successfully by preprocessing the observed data and the residual fields with an integral algorithm, which could produce a more reasonable gradient and therefore lead to better inversion results. Several cases verify the effectiveness of this method. We achieve the simultaneous inversion of relative permittivity and conductivity for on-ground common-offset GPR, and discuss the trade-off between permittivity and conductivity in details. According to the inversion results of test models, it seems that the inversion result of relative permittivity is more credible in most cases.