Polarization of the electromagnetic wavefield has significant implications for the acquisition and interpretation of ground-penetrating radar (GPR) data. Based on the geometrical and physical ...properties of the subsurface scatterer and the physical properties of its surrounding material, strong polarization phenomena might occur. Here, we develop an attribute-based analysis approach to extract and characterize buried utility pipes using two broadside antenna configurations. First, we enhance and extract the utilities by making use of their distinct symmetric nature through the application of a symmetry-enhancing image-processing algorithm known as phase symmetry. Second, we assess the polarization characteristics by calculating two attributes (polarization angle and linearity) using principal component analysis. Combination of attributes derived from these steps into a novel depolarization attribute allows one to efficiently detect and distinguish different utilities present within 3-D GPR data. The performance of our analysis approach is illustrated using synthetic examples and evaluated using field examples (including a dual-configuration 3-D data set) collected across a field site, where detailed ground-truth information is available. Our results demonstrate that the proposed approach allows for a more detailed extraction and combination of utility relevant information compared to approaches relying on single-component data and, thus, eases the interpretation of multicomponent GPR data sets.
The phrase form and function was established in architecture and biology and refers to the idea that form and functionality are closely correlated, influence each other, and co-evolve. We suggest ...transferring this idea to hydrological systems to separate and analyze their two main characteristics: their form, which is equivalent to the spatial structure and static properties, and their function, equivalent to internal responses and hydrological behavior. While this approach is not particularly new to hydrological field research, we want to employ this concept to explicitly pursue the question of what information is most advantageous to understand a hydrological system. We applied this concept to subsurface flow within a hillslope, with a methodological focus on function: we conducted observations during a natural storm event and followed this with a hillslope-scale irrigation experiment. The results are used to infer hydrological processes of the monitored system. Based on these findings, the explanatory power and conclusiveness of the data are discussed. The measurements included basic hydrological monitoring methods, like piezometers, soil moisture, and discharge measurements. These were accompanied by isotope sampling and a novel application of 2-D time-lapse GPR (ground-penetrating radar). The main finding regarding the processes in the hillslope was that preferential flow paths were established quickly, despite unsaturated conditions. These flow paths also caused a detectable signal in the catchment response following a natural rainfall event, showing that these processes are relevant also at the catchment scale. Thus, we conclude that response observations (dynamics and patterns, i.e., indicators of function) were well suited to describing processes at the observational scale. Especially the use of 2-D time-lapse GPR measurements, providing detailed subsurface response patterns, as well as the combination of stream-centered and hillslope-centered approaches, allowed us to link processes and put them in a larger context. Transfer to other scales beyond observational scale and generalizations, however, rely on the knowledge of structures (form) and remain speculative. The complementary approach with a methodological focus on form (i.e., structure exploration) is presented and discussed in the companion paper by Jackisch et al.(2017).
Ground-penetrating radar (GPR) is a popular geophysical method for imaging subsurface structures with a resolution at decimeter scale, which is based on the emission, propagation, and reflection of ...electromagnetic waves. GPR surveys for imaging the cryosphere benefit from the typically highly resistive conditions in frozen ground, resulting in low electromagnetic attenuation and, thus, an increased penetration depth. In permafrost environments, seasonal changes might affect not only GPR performance in terms of vertical resolution, attenuation, and penetration depth, but also regarding the general complexity of data (e.g., due to multiple reflections at thaw boundaries). The experimental setup of our study comparing seasonal differences of summertime thawed and winter- and springtime frozen active layer conditions above ice-rich permafrost allows for estimating advantages and disadvantages of both scenarios. Our results demonstrate major differences in the data and the final GPR image and, thus, will help in future studies to decide about particular survey seasons based on the GPR potential for non-invasive and high-resolution investigations of permafrost properties.
Ground-penetrating radar (GPR) is an established geophysical tool to explore a wide range of near-surface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D ...modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a time-efficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now.
We use a published hydrofacies data set of an aquifer-analog study within fluvio-glacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming fresh-water saturated sediments, we generate synthetic 3D GPR data across this model using novel GPU-acceleration included in the open-source software gprMax. We present a numerical approach to examine 3D wave-propagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other near-surface geophysical techniques.
Display omitted
•Realistic 3D sedimentary model used to generate ground-penetrating radar (GPR) data.•Novel GPU engine in gprMax employed for time-efficient modeling of 3D GPR data.•First publicly available 3D GPR reflection data set and underlying sedimentary model.
We have investigated the potential of combining cross‐hole georadar velocity and attenuation tomography as a method for characterizing heterogeneous alluvial aquifers. A multivariate statistical ...technique, known as k‐means cluster analysis, is used to correlate and integrate information contained in velocity and attenuation tomograms. Cluster analysis allows us to identify objectively the major common trends in the tomographic data and thus to “reduce” the information to a limited number of characteristic parameter combinations. The application of this procedure to two synthetic data sets indicates that it is a powerful tool for converting the complex relationships between the tomographically derived velocity and attenuation structures into a lithologically and hydrologically meaningful zonation of the probed region. In addition, these synthetic examples allow us to evaluate the reliability of further petrophysical parameter estimates. We find that although absolute values of the tomographically inferred petrophysical parameters often differ significantly from the actual parameters, the clustering approach enables us to reliably identify the major trends in the petrophysical properties. Finally, we have applied the approach to a cross‐hole georadar data set collected in a well‐studied alluvial aquifer. A comparison of the clustered tomographic section with well‐log data demonstrates that our approach delineates the hydrostratigraphic zonation.
Electrical resistivity tomography (ERT) aims at imaging the subsurface resistivity distribution and provides valuable information for different geological, engineering, and hydrological applications. ...To obtain a subsurface resistivity model from measured apparent resistivities, stochastic or deterministic inversion procedures may be employed. Typically, the inversion of ERT data results in non-unique solutions; i.e., an ensemble of different models explains the measured data equally well. In this study, we perform inference analysis of model ensembles generated using a well-established global inversion approach to assess uncertainties related to the non-uniqueness of the inverse problem. Our interpretation strategy starts by establishing model selection criteria based on different statistical descriptors calculated from the data residuals. Then, we perform cluster analysis considering the inverted resistivity models and the corresponding data residuals. Finally, we evaluate model uncertainties and residual distributions for each cluster. To illustrate the potential of our approach, we use a particle swarm optimization (PSO) algorithm to obtain an ensemble of 2D layer-based resistivity models from a synthetic data example and a field data set collected in Loon-Plage, France. Our strategy performs well for both synthetic and field data and allows us to extract different plausible model scenarios with their associated uncertainties and data residual distributions. Although we demonstrate our workflow using 2D ERT data and a PSO-based inversion approach, the proposed strategy is general and can be adapted to analyze model ensembles generated from other kinds of geophysical data and using different global inversion approaches.
•PSO for generating ensembles of 2D resistivity models from ERT data.•Statistical tools are used to select appropriate models from the model ensembles.•Cluster analysis to classify globally inverted model solutions.•Classes of models represent different subsurface scenarios including uncertainties.•Residual distribution analysis provide further insights about the inverse problem.
ABSTRACT
Cost‐effective computing capabilities have paved the road for the use of numerical modelling to develop advanced methods and applications of ground‐penetrating radar (GPR). Realistic ...synthetic data and the corresponding modelling techniques, respectively, should consider all subsurface and above‐ground aspects that influence GPR wave propagation and the characteristics of recorded signals. Critical aspects that can be realized in modern GPR modelling tools include heterogeneous and frequency‐dependent material properties, complex structures and interface geometries as well as three‐dimensional antenna models, including the interaction between the antenna and the subsurface. However, realistic noise related to the electronic components of a GPR system or ambient electromagnetic noise is often not considered, or simplified by assuming a white Gaussian noise model which is added to the modelled data. We present an approach to include realistic noise scenarios as typically observed in GPR field data into the flow of modelling synthetic GPR data. In our approach, we extract the noise from recorded GPR traces and add it to the modelled GPR data via a convolution‐based process. We illustrate our methodology using a modelling exercise, where we contaminate a synthetic two‐dimensional GPR dataset with frequency‐dependent noise recorded in an urban environment. Comparing our noise‐contaminated synthetic data with field data recorded in a similar environment illustrates that our method allows the generation of synthetic GPR with realistic noise characteristics and further highlights the limitations of assuming pure white Gaussian noise models.
For near surface geophysical surveys, small-fixed offset loop–loop electromagnetic induction (EMI) sensors are usually placed parallel to the ground surface (i.e., both loops are at the same height ...above ground). In this study, we evaluate the potential of making measurements with a system that is not parallel to the ground; i.e., by positioning the system at different inclinations with respect to ground surface. First, we present the Maxwell theory for inclined magnetic dipoles over a homogeneous half space. By analyzing the sensitivities of such configurations, we show that varying the angle of the system would result in improved imaging capabilities. For example, we show that acquiring data with a vertical system allows detection of a conductive body with a better lateral resolution compared to data acquired using standard horizontal configurations. The synthetic responses are presented for a heterogeneous medium and compared to field data acquired in the historical Park Sanssouci in Potsdam, Germany. After presenting a detailed sensitivity analysis and synthetic examples of such ground conductivity measurements, we suggest a new strategy of acquisition that allows to better estimate the true distribution of electrical conductivity using instruments with a fixed, small offset between the loops. This strategy is evaluated using field data collected at a well-constrained test-site in Horstwalde (Germany). Here, the target buried utility pipes are best imaged using vertical system configurations demonstrating the potential of our approach for typical applications.
•Evaluation of the sensitivity pattern and eddy current for inclined EMI sensors•Advantages of configurations with vertical loops•A bi-directional survey using vertical loop configurations provides realistic apparent conductivity maps.•Synthetic tests and confirmation on field data acquired over buried utility pipes.
•First LCI of FDEM data using both vertical and horizontal MGS constraints.•The presented algorithm can generate pseudo-2D models of adjustable sharpness.•The generated sets of equivalent solutions ...highlight non-uniqueness.
Frequency-domain electromagnetic (FDEM) data are commonly inverted to characterize subsurface geoelectrical properties using smoothness constraints in 1D inversion schemes assuming a layered medium. Smoothness constraints are suitable for imaging gradual transitions of subsurface geoelectrical properties caused, for example, by varying sand, clay, or fluid content. However, such inversion approaches are limited in characterizing sharp interfaces. Alternative regularizations based on the minimum gradient support (MGS) stabilizers can, instead, be used to promote results with different levels of smoothness/sharpness selected by simply acting on the so-called focusing parameter. The MGS regularization has been implemented for different kinds of geophysical data inversion strategies. However, concerning FDEM data, the MGS regularization has only been implemented for vertically constrained inversion (VCI) approaches but not for laterally constrained inversion (LCI) approaches.
We present a novel LCI approach for FDEM data using the MGS regularization for the vertical and lateral direction. Using synthetic and field data examples, we demonstrate that our approach can efficiently and automatically provide a set of model solutions characterized by different levels of sharpness and variable lateral consistencies. In terms of data misfit, the obtained set of solutions contains equivalent models allowing us also to investigate the non-uniqueness of FDEM data inversion.
SUMMARY
Many geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome ...the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a prioriinformation is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally constrained smooth inversion as well as to more standard (i.e., not structurally constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach.
PDF
