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In many near-surface geophysical studies it is now common practice to collect co-located disparate geophysical data sets to explore subsurface structures. Reconstruction of physical parameter distributions underlying the available geophysical data sets usually requires the use of tomographic reconstruction techniques. To improve the quality of the obtained models, the information content of all data sets should be considered during the model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the zonal cooperative inversion methodology based on fuzzy c-means cluster analysis and conventional single-input data set inversion algorithms for the cooperative inversion of data sets with partially co-located model areas. This is done by considering recent developments in fuzzy c-means cluster analysis. Additionally, we show how supplementary a priori information can be incorporated in an automated fashion into the zonal cooperative inversion approach to further constrain the inversion. The only requirement is that this a priori information can be expressed numerically; e.g., by physical parameters or indicator variables. We demonstrate the applicability of the modified zonal cooperative inversion approach using synthetic and field data examples. In these examples, we cooperatively invert S- and P-wave traveltime data sets with partially co-located model areas using water saturation information expressed by indicator variables as additional a priori information. The approach results in a zoned multi-parameter model, which is consistent with all available information given to the zonal cooperative inversion and outlines the major subsurface units. In our field example, we further compare the obtained zonal model to sparsely available borehole and direct-push logs. This comparison provides further confidence in our zonal cooperative inversion model because the borehole and direct-push logs indicate a similar zonation.
Vertical radar profiling (VRP) is a single-borehole geophysical technique, in which the receiver antenna is located within a borehole and the transmitter antenna is placed at one or various offsets from the borehole. Today, VRP surveying is primarily used to derive 1D velocity models by inverting the arrival times of direct waves. Using field data collected at a well-constrained test site in Germany, we evaluated a VRP workflow relying on the analysis of direct-arrival traveltimes and amplitudes as well as on imaging reflection events. To invert our VRP traveltime data, we used a global inversion strategy resulting in an ensemble of acceptable velocity models, and thus, it allowed us to appraise uncertainty issues in the estimated velocities as well as in porosity models derived via petrophysical translations. In addition to traveltime inversion, the analysis of direct-wave amplitudes and reflection events provided further valuable information regarding subsurface properties and architecture. The used VRP amplitude preprocessing and inversion procedures were adapted from raybased crosshole ground-penetrating radar (GPR) attenuation tomography and resulted in an attenuation model, which can be used to estimate variations in electrical resistivity. Our VRP reflection imaging approach relied on corridor stacking, which is a well-established processing sequence in vertical seismic profiling. The resulting reflection image outlines bounding layers and can be directly compared to surface-based GPR reflection profiling. Our results of the combined analysis of VRP, traveltimes, amplitudes, and reflections were consistent with independent core and borehole logs as well as GPR reflection profiles, which enabled us to derive a detailed hydro-stratigraphic model as needed, for example, to understand and model groundwater flow and transport.
In near-surface geophysics, small portable loop-loop electro-magnetic induction (EMI) sensors using harmonic sources with a constant and rather small frequency are increasingly used to investigate the electrical properties of the subsurface. For such sensors, the influence of electrical conductivity and magnetic permeability on the EMI response is well-understood. Typically, data analysis focuses on reconstructing an electrical conductivity model by inverting the out-of-phase response. However, in a variety of near-surface applications, magnetic permeability (or susceptibility) models derived from the in-phase (IP) response may provide important additional information. In view of developing a fast 3D inversion procedure of the IP response for a dense grid of measurement points, we first analyze the 3D sensitivity functions associated with a homogeneous permeable half-space. Then, we compare synthetic data computed using a linear forward-modeling method based on these sensitivity functions with synthetic data computed using full nonlinear forward-modeling methods. The results indicate the correctness and applicability of our linear forward-modeling approach. Furthermore, we determine the advantages of converting IP data into apparent permeability, which, for example, allows us to extend the applicability of the linear forward-modeling method to high-magnetic environments. Finally, we compute synthetic data with the linear theory for a model consisting of a controlled magnetic target and compare the results with field data collected with a four-configuration loop-loop EMI sensor. With this field-scale experiment, we determine that our linear forward-modeling approach can reproduce measured data with sufficiently small error, and, thus, it represents the basis for developing efficient inversion approaches.
Ground-penetrating radar (GPR) is an established geophysical method to explore near-surface sedimentary environments. Interpreting GPR images is largely based on manual procedures following concepts known as GPR facies analysis. We have developed a novel strategy to distinguish GPR facies in a largely automated and more objective manner. First, we calculate 13 textural attributes to quantify GPR reflection characteristics. Then, this database is reduced using principal component analysis. Finally, we image the dominating principal components using composite imaging and classify them using standard clustering methods. The potential of this work-flow is evaluated using a 2D GPR field example collected across stratified glaciofluvial deposits. Our results demonstrate that the derived facies images are well correlated with the composition and the porosity of the sediments as known from independent borehole logs. Our analysis strategy eases and improves the interpretability of GPR data and will help in a variety of geologic and hydrological problems.
Topographic migration of 2D and 3D ground-penetrating radar data considering variable velocities
(2015)
We present a 2D/3D topographic migration scheme for ground-penetrating radar (GPR) data which is able to account for variable velocities by using the root mean square (rms) velocity approximation. We test our migration scheme using a synthetic 2D example and compare our migrated image to the results obtained using common GPR migration approaches. Furthermore, we apply it to 2D and 3D field data. These examples are recorded across common subsurface settings including surface topography and variations in the GPR subsurface velocity field caused by a shallow ground water table. In such field settings, our migration strategy provides well focused images of commonoffset GPR data without the need for a detailed interval velocity model. The synthetic and field examples demonstrate that our topographic migration scheme allows for accurate GPR imaging in the presence of variations in surface topography and subsurface velocity.
Three-dimensional hydrostratigraphic models from ground-penetrating radar and direct-push data
(2011)
Three-dimensional models of hydraulic conductivity and porosity are essential to understand and simulate groundwater flow in heterogeneous geological environments. However, considering the inherent limitations of traditional hydrogeological field methods in terms of resolution, alternative field approaches are needed to establish such 3-D models with sufficient accuracy. In this study, we developed a workflow combining 3-D structural information extracted from ground penetrating radar (GPR) images with 1-D in situ physical-property estimates from direct-push (DP) logging to construct a 3-D hydrostratigraphic model. To illustrate this workflow, we collected an similar to 70 m x 90 m 100 MHz 3-D GPR data set over a shallow sedimentary aquifer system resolving six different GPR facies down to similar to 15 m depth. DP logs of the relative dielectric permittivity, the relative hydraulic conductivity, the cone resistance, the sleeve friction and the pore pressure provided crucial data (1) to establish a GPR velocity model for 3-D depth migration and to check the time-to-depth conversion of the GPR data, and (2) to construct a 3-D hydrostratigraphic model. This model was built by assigning porosity values, which were computed from the DP relative dielectric permittivity logs, and DP relative hydraulic conductivity estimates to the identified GPR facies. We conclude that the integration of 3-D GPR structural images and 1-D DP logs of target physical parameters provides an efficient way for detailed 3-D subsurface characterization as needed, for example, for groundwater flow simulations.
As the Arctic coast erodes, it drains thermokarst lakes, transforming them into lagoons, and, eventually, integrates them into subsea permafrost. Lagoons represent the first stage of a thermokarst lake transition to a marine setting and possibly more saline and colder upper boundary conditions. In this research, borehole data, electrical resistivity surveying, and modeling of heat and salt diffusion were carried out at Polar Fox Lagoon on the Bykovsky Peninsula, Siberia. Polar Fox Lagoon is a seasonally isolated water body connected to Tiksi Bay through a channel, leading to hypersaline waters under the ice cover. The boreholes in the center of the lagoon revealed floating ice and a saline cryotic bed underlain by a saline cryotic talik, a thin ice-bearing permafrost layer, and unfrozen ground. The bathymetry showed that most of the lagoon had bedfast ice in spring. In bedfast ice areas, the electrical resistivity profiles suggested that an unfrozen saline layer was underlain by a thick layer of refrozen talik. The modeling showed that thermokarst lake taliks can refreeze when submerged in saltwater with mean annual bottom water temperatures below or slightly above 0 degrees C. This occurs, because the top-down chemical degradation of newly formed ice-bearing permafrost is slower than the refreezing of the talik. Hence, lagoons may precondition taliks with a layer of ice-bearing permafrost before encroachment by the sea, and this frozen layer may act as a cap on gas migration out of the underlying talik.
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.
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.
In many hydrological applications, ground-wave velocity measurements are increasingly used to map and monitor shallow soil water content. In this study, we propose an automated spectral velocity analysis method to determine the direct ground-wave (DGW) velocity from common midpoint (CMP) or multi-offset ground-penetrating radar (GPR) data. The method introduced in this paper is a variation of the well-known spectral velocity analysis for seismic and GPR reflection events where velocity spectra are computed using different coherency measures along hyperbolas following the normal moveout model. Here, the unnormalized cross-correlation is computed between waveforms across data gathers that are corrected with a linear moveout equation using a predefined range of velocities. Peaks in the resulting velocity spectra identify linear events in the GPR data gathers like DGW events and allow for estimating the corresponding velocities. In addition to obtaining a DGW velocity measurement, we propose a robust method to estimate the associated velocity uncertainties based on the width of the peak in the calculated velocity spectrum. Our proposed method is tested on synthetic data examples to evaluate the influence of subsurface velocity, surveying geometry and signal frequency on the accuracy of estimated ground-wave velocities. In addition, we investigate the influence of such velocity uncertainties on subsequent soil water content estimates using an established petrophysical relationship. Furthermore, we apply our approach to analyse field data, which were collected across a test site in Canada to monitor a wide range of seasonal soil moisture variations. A comparison between our spectral velocity estimates and results derived from manually picked ground-wave arrivals shows good agreement, which illustrates that our spectral velocity analysis is a feasible tool to analyse DGW arrivals in multi-offset GPR data gathers in an objective and more automated manner.
Assessing the human and economic threat introduced by sliding or creeping masses is of major importance in landslide hazard assessment and mitigation. Especially, in the densely populated alpine region unstable hillslopes represent a major hazard to men and infrastructure. Detailed knowledge, especially, of the dominant site-specific controlling factors such as subsurface architecture and geology is thereby key in assessing slope vulnerability. In order to quantify the geological variations at a creeping hillslope in the Austrian Alps, we have collected six 2D refraction seismic profiles. We propose using a layer-based inversion strategy to reconstruct P-wave velocity models from first arrival times. Considering the geological complexity at such sites, the selected inversion approach eases the interpretability of geological structures given intrinsic optimization for only a discrete, user-defined, number of layers. As the applied layer-based inversion approach fits our travel time data equally well as traditional smooth inversion approaches, it represents a feasible mean to summarize the structural complexity often present at such sites. Analysis of the inversion results illustrates that bedrock topography clearly deviates from a previously assumed planar surface and exhibits distinct variations across the slope extension. Bedrock topography additionally impacts the intermediate geological units and, thus, this information is critical for further analyses such as geomechanical modeling. (C) 2012 Elsevier B.V. All rights reserved.
In near- surface geophysics, ground-based mapping surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we have developed a multiscale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the a trous algorithm, which allows to efficiently compute a multiscale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, whereas our archaeogeophysical case study from northeast Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, whereas our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multiscale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.
For a detailed characterization of near-surface environments, geophysical techniques are increasingly used to support more conventional point-based techniques such as borehole and direct-push logging. Because the underlying parameter relations are often complex, site-specific, or even poorly understood, a remaining challenging task is to link the geophysical parameter models to the actual geotechnical target parameters measured only at selected points. We propose a workflow based on nonparametric regression to establish functional relationships between jointly inverted geophysical parameters and selected geotechnical parameters as measured, for example, by different borehole and direct-push tools. To illustrate our workflow, we present field data collected to characterize a near-surface sedimentary environment Our field data base includes crosshole ground penetrating radar (GPR), seismic P-, and S-wave data sets collected between 25 m deep boreholes penetrating sand- and gravel dominated sediments. Furthermore, different typical borehole and direct-push logs are available. We perform a global joint inversion of traveltimes extracted from the crosshole geophysical data using a recently proposed approach based on particle swarm optimization. Our inversion strategy allows for generating consistent models of GPR, P-wave, and S-wave velocities including an appraisal of uncertainties. We analyze the observed complex relationships between geophysical velocities and target parameter logs using the alternating conditional expectation (ACE) algorithm. This nonparametric statistical tool allows us to perform multivariate regression analysis without assuming a specific functional relation between the variables. We are able to explain selected target parameters such as characteristic grain size values or natural gamma activity by our inverted geophysical data and to extrapolate these parameters to the inter-borehole plane covered by our crosshole experiments. We conclude that the ACE algorithm is a powerful tool to analyze a multivariate petrophysical data base and to develop an understanding of how a multi-parameter geophysical model can be linked and translated to selected geotechnical parameters.
Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006
In this paper, we present an efficient kinematic ground-penetrating radar (GPR) surveying setup using a self- tracking total station (TTS). This setup combines the ability of modern GPR systems to interface with Global Positioning System (GPS) and the capability of the employed TTS system to immediately make the positioning information available in a standardized GPS data format. Wireless communication between the GPR and the TTS system is established by using gain variable radio modems. Such a kinematic surveying setup faces two major potential limitations. First, possible crosstalk effects between the GPR and the positioning system have to be evaluated. Based on multiple walkaway experiments, we show that, for reasonable field setups, instrumental crosstalk has no significant impact on GPR data quality. Second, we investigate systematic latency (i.e., the time delay between the actual position measurement by TTS and its fusion with the GPR data) and its impact on the positional precision of kinematically acquired 2-D and 3-D GPR data. To quantify latency for our kinematic survey setup, we acquired forward-reverse profile pairs across a well-known subsurface target. Comparing the forward and reverse GPR images using three fidelity measures allows determining the optimum latency value and correcting for it. Accounting for both of these potential limitations allows us to kinematically acquire high- quality and high-precision GPR data using off-the-shelf instrumentation without further hardware modifications. Until now, these issues have not been investigated in detail, and thus, we believe that our findings have significant implications also for other geophysical surveying approaches.
Geophysical techniques offer the potential to tomographically image physical parameter variations in the ground in two or three dimensions. Due to the limited number and accuracy of the recorded data, geophysical model generation by inversion suffers ambiguity. Linking the model generation process of disparate data by jointly inverting two or more data sets allows for improved model reconstruction. Fully nonlinear inversion using optimization techniques searching the solution space of the inverse problem globally enables quantitative assessment of the ambiguity inherent to the model reconstruction. We used two different multiobjective particle swarm optimization approaches to jointly invert synthetic crosshole tomographic data sets comprising radar and P-wave traveltimes, respectively. Beginning with a nonlinear joint inversion founded on the principle of Pareto optimality and game theoretic concepts, we obtained a set of Pareto-optimal solutions comprising commonly structured radar and P-wave velocity models for low computational costs. However, the efficiency of the approach goes along with some risk of achieving a final model ensemble not adequately illustrating the ambiguity inherent to the model reconstruction process. Taking advantage of the results of the first approach, we inverted the database using a different nonlinear joint-inversion approach reducing the multiobjective optimization problem to a single-objective one. Computational costs were significantly higher, but the final models were obtained mutually independently allowing for objective appraisal of model parameter determination. Despite the high computational effort, the approach was found to be an efficient nonlinear joint-inversion formulation compared to what could be extracted from individual nonlinear inversions of both data sets.
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. (C) 2015 Elsevier B.V. Pill rights reserved.
The vertical radar profiling (VRP) technique uses surface-to-borehole acquisition geometries comparable to vertical seismic profiling (VSP). Major differences between the two methods do arise due to the fundamentally differing nature of the velocity-depth gradients and transmitter/receiver directivities. Largely for this reason, VRP studies have so far essentially been limited to the reconstruction of velocity-depth profiles by inverting direct arrival times from single-offset VRP surveys. In this study, we investigate the potential to produce high-resolution subsurface reflection images from multi-offset VRP data. Two synthetic data sets are used to evaluate a processing strategy suitably adapted from VSP processing. Despite the fundamental differences between VRP and VSP data, we found that our processing approach is capable of reconstructing subsurface structures of comparable complexity to those routinely imaged by VSP data. Finally, we apply our processing flow to two multi-offset VRP data sets recorded at a well constrained hydrogeophysical test site in SW-Germany. The inferred VRP images are compared with high-quality surface georadar reflection images and lithological logs available at the borehole locations. We find that the VRP images are in good agreement with the surface georadar data and reliably detect the major lithological boundaries. Due to the significantly shorter ray-paths, the depth penetration of the VRP data is, however, considerably higher than that of the surface georadar data. VRP reflection images thus provide an effective means for the depth-calibration and extension of conventional surface georadar data in the vicinity of boreholes.
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.