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In an attempt to map the shallow geometry of the Maleme Fault Zone (North Island, New Zealand) and estimate vertical displacements of selected fault strands, we have collected 2D and 3D georadar data using 100 MHz antennae. The 2D data consisted of three parallel georadar lines recorded perpendicular to the axis of the well-defined graben of the Maleme Fault Zone. These similar to 160 in long lines, which were 7.5 m apart, crossed several fault strands on either side of the graben axis. The processed georadar sections revealed two prominent parallel reflections that originated from the boundaries of Late Pleistocene lacustrine and tephra deposits. Distinct vertical offsets of these reflections allowed us to estimate displacernents at individual fault strands across the entire inner graben. The total displacements represented by these offsets was similar to 10-20% greater than that inferred from geomorphological studies, thus demonstrating the limitations of surface observations for determining cumulative fault movements. The 3D georadar data set, recorded across an area of similar to 70x similar to 20 in to one side of the graben axis, provided key details on individual fault strands. For the 3D visualization of fault-related structures, various spatial attribute analyses based on the cosine of the instantaneous phase proved to be useful
Inversions of an individual geophysical data set can be highly nonunique, and it is generally difficult to determine petrophysical parameters from geophysical data. We show that both issues can be addressed by adopting a statistical multiparameter approach that requires the acquisition, processing, and separate inversion of two or more types of geophysical data. To combine information contained in the physical-property models that result from inverting the individual data sets and to estimate the spatial distribution of petrophysical parameters in regions where they are known at only a few locations. we demonstrate the potential of the fuzzy c-means (FCM) clustering technique. After testing this new approach on synthetic data, we apply it to limited crosshole georadar, crosshole seismic, gamma-log, and slug-test data acquired within a shallow alluvial aquifer. The derived multiparameter model effectively outlines the major sedimentary units observed in numerous boreholes and provides plausible estimates for the spatial distributions of gamma-ray emitters and hydraulic conductivity
Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006
Hydrogeophysik : Erkundungen und Sicherung der Ressource Wasser : Antrittsvorlesung 2006-06-01
(2006)
Die weltweite Wasserversorgung basiert zu einem überwiegenden Teil auf Grundwasser. Die Erkundung, der Schutz, die nachhaltige Nutzung sowie die eventuelle Sanierung dieser Grundwasserressourcen sind somit global von fundamentalem gesellschaftlichem Interesse. Bei vielen dieser grundwasserbezogenen Fragestellungen ist häufig eine effiziente und detaillierte Charakterisierung des Untergrundes notwendig. Geophysikalische Messverfahren liefern Abbilder der physikalischen Eigenschaften, wie beispielsweise des elektrischen Widerstandes, die wichtige Informationen über den geometrischen und stofflichen Aufbau des verborgenen Untergrundes liefern. In der Vorlesung wird gezeigt, wie die Verfahren der Angewandten Geophysik auf Fragestellungen hinsichtlich der Präsenz, Ausbreitung und Qualität der Ressource Grundwasser eingesetzt werden können. Darüber hinaus werden aktuelle Forschungsthemen und offene Fragen angesprochen.
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.
There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band- limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.
We present cross-hole P- and S-wave seismic experiments that have been performed along a similar to 100 m long transect for the detailed characterization of a contaminated sedimentary site (Bitterfeld research test site, Germany). We invert the corresponding first break arrival times for the P- and S-wave velocity structure and compare two different strategies to interpret these models in terms of pertinent lithological and geotechnical parameter variations. The first (common) approach is based on directly translating the tomographic velocity models into the parameters of interest (e.g., elastic moduli). The second (zonal) approach first reduces the tomographic parameter information to a limited number of characteristic velocity combinations via k-means cluster analysis. Then, for each zone (cluster) further parameters including uncertainties can be estimated. In the presented case study, Our results indicate that the zonal approach provides an effective means for the integrated interpretation of different co-located data.
Three-dimensional (3D) ground-penetrating radar (GPR) represents an efficient high-resolution geophysical surveying method allowing to explore archaeological sites in a non-destructive manner. To effectively analyze large 3D GPR data sets, their combination with modern visualization techniques (e.g., 3D isoamplitude displays) has been acknowledged to facilitate interpretation beyond classical time-slice analysis. In this study, we focus on the application of data attributes (namely energy, coherency, and similarity), originally developed for petroleum reservoir related problems addressed by reflection seismology, to emphasize temporal and spatial variations within GPR data cubes. Based on two case studies, we illustrate the potential of such attribute based analyses towards a more comprehensive 3D GPR data interpretation. The main goal of both case studies was to localize and potentially characterize tombs inside medieval chapels situated in the state of Brandenburg, Germany. By comparing the calculated data attributes to the conventionally processed data cubes, we demonstrate the superior interpretability of the coherency and the similarity attribute for target identification and characterization.
We have collected magnetic, 3D ground-penetrating-radar (GPR), and topographic data at an archaeological site within the Palace Garden of Paretz, Germany. The survey site covers an area of approximately 35 x 40 m across a hill structure (dips of up to 15 degrees) that is partly covered by trees. The primary goal of this study was to detect and locate the remains of ancient architectural elements, which, from historical records, were expected to be buried in the subsurface at this site. To acquire our geophysical data, we used a recently developed surveying approach that combines the magnetic and GPR instrument with a tracking total station (TTS). Besides efficient data acquisition, this approach provides positional information at an accuracy within the centimeter range. At the Paretz field site, this information was critical for processing and analyzing our geophysical data (in particular, GPR data) and enabled us to generate a high-resolution digital terrain model (DTM) of the surveyed area. Integrated analysis and interpretation based on composite images of the magnetic, 3D GPR, and high-resolution DTM data as well as selected attributes derived from these data sets allowed us to outline the remains of an artificial grotto and temple. Our work illustrates the benefit of using multiple surveying technologies, analyzing and interpreting the resulting data in an integrated fashion. It further demonstrates how modern surveying solutions allow for efficient, accurate data acquisition even in difficult terrain.
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.
Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing.
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.
Decomposition of geophysical signals (e.g., seismic and ground-penetrating radar data) into the time-frequency domain can provide valuable information for advanced interpretation (e.g., tuning effects) and processing (e.g., inverse Q-filtering). The quality of these subsequent processing steps is strongly related to the resolution of the selected time-frequency representation (TFR). In this study, we introduce a high-resolution spectral decomposition approach representing an extension of the recently proposed Tree-Based Pursuit (TBP) method. TBP significantly reduces the computational cost compared to the well known Matching Pursuit (MP) technique by introducing a tree structure prior to the actual matching procedure. Following the original implementation of TBP, we additionally incorporate waveforms commonly used in geophysical data processing and present an alternative approach to take phase shifts into account. Application of the proposed method to synthetic data and comparison of the results with other typically used decomposition approaches, illustrate the ability of our approach to provide decomposition results highly localized in both time and frequency. Applying our procedure to field GPR data illustrates its applicability to real data and provides examples for potential applications such as analyzing thin-bed responses and modulating the data frequency content.
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.
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.
Mapping hydrological parameter distributions in high resolution is essential to understand and simulate groundwater flow and contaminant transport. Of particular interest is surface-based ground-penetrating radar (GPR) reflection imaging in electrically resistive sediments because of the expected close link between the subsurface water content and the dielectric permittivity, which controls GPR wave velocity and reflectivity. Conventional tools like common midpoint (CMP) velocity analysis provide physical parameter models of limited resolution only. We present a novel reflection amplitude inversion workflow for surface-based GPR data capable of resolving the subsurface dielectric permittivity and related water content distribution with markedly improved resolution. Our scheme is an adaptation of a seismic reflection impedance inversion scheme to surface-based GPR data. Key is relative-amplitude-preserving data preconditioning including GPR deconvolution, which results in traces with the source-wavelet distortions and propagation effects largely removed. The subsequent inversion for the underlying dielectric permittivity and water content structure is constrained by in situ dielectric permittivity data obtained by direct-push logging. After demonstrating the potential of our novel scheme on a realistic synthetic data set, we apply it to two 2-D 100 MHz GPR profiles acquired over a shallow sedimentary aquifer resulting in water content images of the shallow (3-7 m depth) saturated zone having decimeter resolution.
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.
Crosshole traveltime tomography using particle swarm optimization a near-surface field example
(2012)
Particle swarm optimization (PSO) is a relatively new global optimization approach inspired by the social behavior of bird flocking and fish schooling. Although this approach has proven to provide excellent convergence rates in different optimization problems, it has seldom been applied to inverse geophysical problems. Until today, published geophysical applications mainly focus on finding an optimum solution for simple, 1D inverse problems. We have applied PSO-based optimization strategies to reconstruct 2D P-wave velocity fields from crosshole traveltime data sets. Our inversion strategy also includes generating and analyzing a representative ensemble of acceptable models, which allows us to appraise uncertainty and nonuniqueness issues. The potential of our strategy was tested on field data collected at a well-constrained test site in Horstwalde, Germany. At this field site, the shallow subsurface mainly consists of sand- and gravel-dominated glaciofluvial sediments, which, as known from several boreholes and other geophysical experiments, exhibit some well-defined layering at the scale of our crosshole seismic data. Thus, we have implemented a flexible, layer-based model parameterization, which, compared with standard cell-based parameterizations, allows for significantly reducing the number of unknown model parameters and for efficiently implementing a priori model constraints. Comparing the 2D velocity fields resulting from our PSO strategy to independent borehole and direct-push data illustrated the benefits of choosing an efficient global optimization approach. These include a straightforward and understandable appraisal of nonuniqueness issues as well as the possibility of an improved and also more objective interpretation.