Urs Böniger

On the Potential of Kinematic GPR Surveying Using a Self-Tracking Total Station: Evaluating System Crosstalk and Latency

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.

Subsurface Utility Extraction and Characterization: Combining GPR Symmetry and Polarization Attributes

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.

Integrated data analysis at an archaeological site: A case study using 3D GPR, magnetic, and high-resolution topographic data

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 35×40 m across a hill structure (dips of up to 15°) 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 a...

Topographic migration of 2D and 3D ground-penetrating radar data considering variable velocities

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.

Multi-Offset Vertical Radar Profiling for Subsurface Reflection Imaging

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 recor...

Join global inversion of GPR and P-wave seismic traveltimes using particle swarm optimization

Particle swarm optimization (PSO) is a relatively new global optimization approach inspired by the social behavior of birds and fishes. Although this approach has proven to provide excellent convergence rates in different optimization problems, it has seldom been used in geophysical inversion. Here, we propose a PSO-based inversion strategy to jointly invert GPR and P-wave seismic traveltimes from co-located crosshole experiments. Using a synthetic data example, we demonstrate the potential of our approach. Comparing our results to the input models as well as to velocity models found by separately inverting the data using a standard linearized inversion approach, illustrates the benefits of using an efficient global optimization approach for such a joint inversion problem. These include a straightforward appraisal of uncertainty, non-uniqueness, and resolution issues as well as the possibility of an improved and more objective interpretation.

Topographic migration of GPR data with variable velocities

The topographic relief of a typical ground-penetrating radar (GPR) survey can easily be in the range of the target depth. In complex subsurface settings, this requires the application of topographic migration schemes to reliably image subsurface structures. Furthermore, a shallow ground water layer introduces significant vertical variations in GPR propagation velocity, which should be considered during migration to accurately image subsurface structures. In this paper, we present a modified topographic migration scheme, which is able to account for vertical variable velocities by using the root-mean-square (rms) velocity approximation. We evaluate our migration scheme by using synthetic and field data, which represent typical near-surface sedimentary structures often investigated using GPR. These examples demonstrate that a significant improvement in structural imaging quality is achieved by considering rms velocities during topographic migration.

Symmetry based 3D GPR feature enhancement and extraction

The efficient analysis of high-resolution 3D GPR data sets is of increasing importance given todays possibilities to acquire large and dense data volumes. In order to reduce data complexity and enhance the features of interest, attribute based analysis, a well-established field in reflection seismology, has received growing interest also in the GPR community. Here, we present a novel GPR attribute called phase symmetry, which is adapted from image processing. We believe that this attribute is well-suited for analyzing 3D GPR data sets. In this study, we introduce the basic concepts of phase symmetry. Using two synthetic examples and comparing phase symmetry to the well- known Canny edge detector, we illustrate that phase symmetry also provides high quality results in the presence of significant noise and smoothly varying anomalies. Using two 3D GPR field examples collected to detect buried utilities and archaeologically relevant features, respectively, demonstrates the applicability of phase symmetry to real GPR data and illustrates that this attribute is an effective tool in order to extract symmetric features embedded within a heterogeneous background. Additionally, we show that phase symmetry can be combined with the similarity attribute to jointly emphasize event symmetry and waveform similarity.

Denoising magnetic data using steering kernel regression

Ground-based magnetic surveying is a common geophysical method to explore near-surface environments in a non-destructive manner. In many typical applications (such as archaeological prospection), the resulting anomaly maps are often characterized by low signal-to-noise ratios and, thus, the suppression of noise is a key step in data processing. Here, we propose the steering kernel regression (SKR) method to denoise magnetic data sets. SKR has been recently developed to suppress random noise in images and video sequences. The core of the method is the steering kernel function which represents a robust estimate of local image structure. Using such a kernel within an iterative regression based denoising framework, helps to minimize image blurring and to preserve the underlying structures such as edges and corners. Because such filter characteristics are desirable for random noise attenuation in potential field data sets, we apply the SKR method for processing high-resolution ground-based magnetic data as they are typically collected in archaeological applications. We test and evaluate the SKR method using synthetic and field data examples and also compare it to more commonly employed denoising strategies relying, for example, on fixed filter masks (e.g., Gaussian filters). Our results show that the SKR method is successful in removing random and acquisition related noise present in our data. Concurrently, it preserves the local image structure including the amplitudes of anomalies. As demonstrated by derivative based transformations, the mentioned filter characteristics significantly impact subsequent processing steps and, therefore, result in an improved analysis and interpretation of magnetic data. Thus, the method can be considered as a promising and novel approach for denoising ground-based magnetic data.

Steering kernel regression: An adaptive denoising tool to process GPR data

The recently introduced steering kernel regression (SKR) framework was originally developed to attenuate random noise in images and video sequences. The core of the method is the steering kernel function which incorporates a robust local estimate of image structure into the denoising framework. This helps to minimize image blurring and to preserve edges and corners. As such filter characteristics are also desirable for random noise attenuation in ground-penetrating radar (GPR) data, we propose to adopt the SKR method for processing GPR data. We test and evaluate this denoising method using different GPR data examples. Our results show that SKR is successful in removing random noise present in our data sets. Concurrently, it preserves local image structure and amplitudes. Thus, the method can be considered as a promising and novel approach for denoising GPR data.