Refine
Year of publication
Language
- English (54) (remove)
Is part of the Bibliography
- yes (54) (remove)
Keywords
- Ground-penetrating radar (4)
- Ground penetrating radar (3)
- Controlled source electromagnetics (CSEM) (2)
- Electromagnetics (2)
- Global inversion (2)
- Inverse theory (2)
- Inversion (2)
- ground-penetrating radar (2)
- preferential flow (2)
- tracer (2)
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.
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.
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.
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.
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.
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.
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.
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.