Multi-offset Ground-penetrating Radar Research Articles

Indirect joint petrophysical inversion of shallow-seismic and multi-offset ground-penetrating radar field data

SUMMARY In near-surface surveys, shallow-seismic and ground-penetrating radar (GPR) full-waveform inversions (FWIs) have received increasing attention because of their ability to reconstruct high-resolution subsurface models. However, they have different sensitivities to the same targets and thus may yield conflicting geophysical parameter models. To solve this issue, we have developed an indirect joint petrophysical inversion (JPI) integrating shallow-seismic and multi-offset GPR data. These data are used to reconstruct porosity and saturation whereby we use only strong sensitivities between petrophysical and geophysical parameters. To promote its field application, we proposed an input strategy to avoid measuring rock matrix parameters and make indirect JPI more robust. We apply indirect JPI to the field data acquired in Rheinstetten, Germany and find that it reveals the mechanical, electrical and petrophysical properties more reliably than individual inversions. The reconstructed models are assessed by direct-push technology, borehole sample measurements and migrated GPR image. Indirect JPI can fit seismic and GPR observed data simultaneously and provide consistent multiparameter models, which are hard to achieve by FWIs and individual petrophysical inversions. We also find that the method is robust when there are uncertainties in petrophysical a priori information. Overall, the field example proves the great potential of using indirect JPI to solve real-world problems.

Continuous Automatic Estimation of Volumetric Water Content Profile During Infiltration Using Sparse Multi-Offset GPR Data

Ground-penetrating radar (GPR) is a non-destructive and non-invasive geophysical survey method that has been used to characterize soil volumetric water content (VWC) dynamics. An array antenna GPR system was used to collect nearly seamless, time-lapse multi-offset GPR data during an in-situ infiltration test on sand dunes with limited traces. Because the data volume was significant, an approach was utilized to automatically determine electromagnetic wave velocities from sparse common midpoint (CMP) data using standard velocity analysis, such as semblance analysis. The objective of this study was to develop a methodology that allows one to automatically perform velocity analysis by interpolating sparse CMP data obtained with the array GPR system. The proposed method determined the optimal normal moveout velocity values and the removal range of the F-K zone pass filter that minimized errors between the original and interpolated CMP data using leave-one-out cross-validation (LOOCV). After interpolating the sparse CMP data with the F-K zone pass filter, semblance analysis was used to determine the time-lapse velocity structure of the soil profile during water infiltration. The velocity data were converted to VWC data based on the Topp equation, which relates the soil VWC to the soil dielectric constant. The proposed method was tested using CMP data obtained via numerical simulation and experiments. The VWC profile from the proposed approach matched well with the independently observed VWC profiles obtained from an invasive probe-type soil moisture sensor.

Full-waveform inversion of ground-penetrating radar data in frequency-dependent media involving permittivity attenuation

SUMMARYFull-waveform inversion (FWI) of ground-penetrating radar (GPR) data has received particular attention in the past decade because it can provide high-resolution subsurface models of dielectric permittivity and electrical conductivity. In most GPR FWIs, these two parameters are regarded as frequency independent, which may lead to false estimates if they strongly depend on frequency, such as in shallow weathered zones. In this study, we develop frequency-dependent GPR FWI to solve this problem. Using the τ-method introduced in the research of viscoelastic waves, we define the permittivity attenuation parameter to quantify the attenuation resulting from the complex permittivity and to modify time-domain Maxwell’s equations. The new equations are self-adjoint so that we can use the same forward engine to back-propagate the adjoint sources and easily derive model gradients in GPR FWI. Frequency dependence analysis shows that permittivity attenuation acts as a low-pass filter, distorting the waveform and decaying the amplitude of the electromagnetic waves. The 2-D synthetic examples illustrate that permittivity attenuation has low sensitivity to the surface multioffset GPR data but is necessary for a good reconstruction of permittivity and conductivity models in frequency-dependent GPR FWI. As a comparison, frequency-independent GPR FWI produces more model artefacts and hardly reconstructs conductivity models dominated by permittivity attenuation. The 2-D field example shows that both FWIs reveal a triangle permittivity anomaly which proves to be a refilled trench. However, frequency-dependent GPR FWI provides a better fit to the observed data and a more robust conductivity reconstruction in a high permittivity attenuation environment. Our GPR FWI results are consistent with previous GPR and shallow-seismic measurements. This research greatly expands the application of GPR FWI in more complicated media.

Development of a workflow for processing ground-penetrating radar data from multiconcurrent receivers

Ground-penetrating radar (GPR) systems with multiconcurrent sampling receivers can rapidly acquire dense multioffset GPR data, which are not feasible using typical common-offset (CO) GPR systems with a single fixed offset transmitter-receiver pair. Multioffset GPR data from these new multiconcurrent receiver systems have the potential to be used to create detailed subsurface velocity models and enhanced reflection sections. These are important features that can improve qualitative and quantitative interpretation of GPR data. To realize these benefits and to deal with the large amount of multioffset data generated by these new systems, we have developed an automated and customized data processing workflow. There are three key algorithms that we have developed as part of our workflow, which is crucial for processing large volume, multioffset GPR data so as: first, to efficiently correct and manage time misalignments from multiconcurrent receivers; second, to carry out trace balancing of common-midpoint data for semblance analysis; and third, to automate the velocity analysis step. We showcase our processing workflow using two field data sets acquired using a multiconcurrent sampling receiver GPR system consisting of one transmitter and seven receivers. The field data were collected at two different locations: a site using a system with a 500 MHz center frequency and another site using a system with a 1000 MHz center frequency. We have determined, with both data sets, that our processing workflow could produce automated stacking velocity fields and enhanced zero-offset reflection cross sections. These benefits increase the information that can be used for interpretation (compared with conventional CO data) and can form the basis of further processing steps such as migration. As the cost of these multiconcurrent sampling receiver systems decreases over time, we anticipate their use, and the acquisition of dense multioffset GPR data, to become much more commonplace.

Indirect joint petrophysical inversion of synthetic shallow-seismic and multi-offset ground-penetrating radar data

SUMMARYBoth full-waveform inversion (FWI) of shallow-seismic and ground-penetrating radar (GPR) surface recordings have received particular attention in the past decade since they can reconstruct seismic and electromagnetic properties at high resolution. For consistent near-surface imaging, seismic and GPR wavefields can be combined by joint petrophysical inversion (JPI) using classical time-domain FWI. In conventional JPI of wavefields, both shallow-seismic and multi-offset GPR data contribute to reconstructing the same petrophysical parameters. In this paper, we show that seismic and GPR wavefields have different sensitivities to these parameters assuming the widely established petrophysical model combining the Gassmann fluid substitution model, the complex refractive index model and Archie’s equation. Based on this observation, we propose a new coupling strategy of petrophysical parameters which we call indirect JPI. In indirect JPI, seismic data is primarily used for porosity reconstruction, while GPR data is used only for saturation reconstruction. Unlike conventional JPI, we first update the seismic and GPR parameters using non-petrophysical parametrizations and then transform the most reliable estimates to petrophysical parameters. 2-D synthetic tests show that indirect JPI can provide more accurate and consistent results than conventional JPI. In addition, indirect JPI is more robust when uncertainties exist in petrophysical a priori knowledge. More importantly, indirect JPI has the flexibility to integrate different types of seismic and electromagnetic waves and acquisition geometries depending on the target of interest, resulting in the best solution. Indirect JPI has been proven to be a promising approach for multiparameter reconstructions (seven parameters in this study).

Reconstruction of historical surface mass balance, 1984–2017 from GreenTrACS multi-offset ground-penetrating radar

AbstractWe present continuous estimates of snow and firn density, layer depth and accumulation from a multi-channel, multi-offset, ground-penetrating radar traverse. Our method uses the electromagnetic velocity, estimated from waveform travel-times measured at common-midpoints between sources and receivers. Previously, common-midpoint radar experiments on ice sheets have been limited to point observations. We completed radar velocity analysis in the upper ~2 m to estimate the surface and average snow density of the Greenland Ice Sheet. We parameterized the Herron and Langway (1980) firn density and age model using the radar-derived snow density, radar-derived surface mass balance (2015–2017) and reanalysis-derived temperature data. We applied structure-oriented filtering to the radar image along constant age horizons and increased the depth at which horizons could be reliably interpreted. We reconstructed the historical instantaneous surface mass balance, which we averaged into annual and multidecadal products along a 78 km traverse for the period 1984–2017. We found good agreement between our physically constrained parameterization and a firn core collected from the dry snow accumulation zone, and gained insights into the spatial correlation of surface snow density.

Two-dimensional Time-domain Full Waveform Inversion of On-ground Common-offset GPR Data Based on Integral Preprocessing

Full waveform inversion (FWI) is an advanced inversion technique for ground penetrating radar (GPR), which could provide quantitative, high-resolution subsurface imaging. FWI has been used widely to process crosshole and on-ground multi-offset GPR data, but its application to on-ground common-offset GPR data is more difficult and being developed. This is mainly because that on-ground common-offset GPR has much less coverage of the subsurface and mainly includes reflective information. The application of conventional FWI to pure reflection data in the absence of a highly accurate starting velocity model is difficult. Here, we demonstrate a means of achieving this successfully by preprocessing the observed data and the residual fields with an integral algorithm, which could produce a more reasonable gradient and therefore lead to better inversion results. Several cases verify the effectiveness of this method. We achieve the simultaneous inversion of relative permittivity and conductivity for on-ground common-offset GPR, and discuss the trade-off between permittivity and conductivity in details. According to the inversion results of test models, it seems that the inversion result of relative permittivity is more credible in most cases.

Reflection tomography of time-lapse GPR data for studying dynamic unsaturated flow phenomena

Abstract. Ground-penetrating radar (GPR) reflection tomography algorithms allow non-invasive monitoring of water content changes resulting from flow in the vadose zone. The approach requires multi-offset GPR data that are traditionally slow to collect. We automate GPR data collection to reduce the survey time significantly, thereby making this approach to hydrologic monitoring feasible. The method was evaluated using numerical simulations and laboratory experiments that suggest reflection tomography can provide water content estimates to within 5 % vol vol−1–10 % vol vol−1 for the synthetic studies, whereas the empirical estimates were typically within 5 %–15 % of measurements from in situ probes. Both studies show larger observed errors in water content near the periphery of the wetting front, beyond which additional reflectors were not present to provide data coverage. Overall, coupling automated GPR data collection with reflection tomography provides a new method for informing models of subsurface hydrologic processes and a new method for determining transient 2-D soil moisture distributions.

Semi-automatic determination of layer depth, permittivity and moisture content for unbound granular pavements using multi-offset 3-D GPR

ABSTRACT A semi-automated multi-offset ground penetrating radar (GPR) analysis method called Ray-path Modelling Coherence (RM–C) was developed and field tested. It was used to quasi-continuously predict the depth, permittivity and volumetric moisture content of unbound granular (UBG) layers along a pavement site during two investigations using ground-coupled measurements from a 3-D GPR array. Predictions from the later site visit were compared to physical measurements of layer depth and moisture content at a number of locations along the site, which in general agreed well. RM-C permittivity predictions were compared to results from time domain reflectometery (TDR) sensors and diffraction hyperbola fitting of buried reflectors. The new approach reported higher permittivity values compared to these other methods, although showed similar trends in permittivity change with respect to pavement depth and ⁠variation over time. Reproducibility of the RM–C analysis was confirmed by comparing layer depth and moisture content predictions for repeat runs along the site, although occasional discrepancies were observed.

Inverse Methods to Improve Accuracy of Water Content Estimates from Multi-offset GPR

Ground penetrating radar (GPR) is a powerful hydrogeophysical tool for estimating porosity and water content of geologic materials using radar wave velocities and appropriate petrophysical relations. In substrates with more than one layer of interest, surface-based multi-offset measurements require careful analysis to accurately retrieve physical properties for each layer. Frequently, Dix inversion is used to calculate interval velocities, however the assumptions and limitations of this approach are widely known. In particular for survey geometries and targets encountered with GPR, the assumptions inherent to Dix inversion are readily violated, and therefore inverse modeling is required to avoid velocity error. While the impact on velocity incurred by violating the assumptions of Dix inversion is well understood, the effects on water content estimates have not been widely reported and are therefore the subject of this work. In a subsurface representative of an unsaturated zone overlying an aquifer, error in excess of 50% in water content due to violating the assumptions of Dix inversion is demonstrated. Examples are shown using raytracing inversion to solve for subsurface water content structure that avoids the errors inherent to Dix inversion. These results are intended to underscore the importance of minimizing assumptions and using more correct physics when analyzing multi-offset GPR data, particularly due to the large potential errors that may be encountered if water content estimation is the main objective.

Estimation of porosity and water saturation in dual-porosity pyroclastic deposits from joint analysis of compression, shear, and electromagnetic velocities

In situ measurements of porosity and water saturation of pyroclastic deposits have the potential to improve interpretations of geology and hydrology in volcanic regions, and to provide more accurate estimates of dense rock equivalent for volcanic eruptions. However, rock-property models must consider the dual-porosity structure of pyroclastic deposits (i.e., vesicles within pumices and intergranular pores). Vesicularity, intergranular porosity, and water saturation all affect the density, elasticity, and dielectric properties of pyroclastic materials, which control seismic and electromagnetic velocities. The data from active seismic and ground-penetrating radar (GPR) techniques may improve porosity and water saturation estimation if the responses of seismic and electromagnetic velocities to porosity and water saturation variations are complementary in pyroclastic deposits. We developed a dual-porosity petrophysical model to calculate seismic and electromagnetic velocities of pyroclastic deposits with known intergranular porosity, vesicularity, and water saturation, and we tested our ability to estimate porosity and water saturation from field measurements of seismic and electromagnetic velocities in pyroclastic deposits at Mount St. Helens, Washington, USA. Our petrophysical model demonstrates that seismic velocities are more sensitive to intergranular porosity and less sensitive to vesicularity; electromagnetic velocity is primarily controlled by volumetric water content. In a multioffset GPR and active seismic case study, seismic first-arrival traveltime tomography and multichannel analysis of surface waves can resolve high-velocity anomalies caused by porosity reduction. Joint petrophysical inversion of electromagnetic and seismic velocities indicates that although intergranular porosity and water saturation are well-constrained (i.e., standard deviations of approximately 0.05), quantitative estimates of vesicularity remain less certain (i.e., standard deviation of approximately 0.21), due to weak sensitivity.

Two-Dimensional Reverse-Time Migration Applied to GPR With a 3-D-to-2-D Data Conversion

Reverse-time migration (RTM) has shown its advantages over other conventional migration algorithms for ground-penetrating radar (GPR) imaging. RTM is preferred to be implemented in the computationally attractive 2-D domain, whereas a real measurement can only be conducted in a 3-D domain. Thus, we propose an asymptotic 3-D-to-2-D data conversion filter in the frequency domain for preprocessing of the recorded data for 2-D RTM. The accuracy of the data conversion filter is verified by two numerical tests on a homogeneous and a layered model. Then, we evaluate the effectiveness of the data conversion filter on the imaging result of 2-D RTM, which is applied to simulated multioffset GPR data from a buried pipe model. With the filter, subsurface image by the 2-D RTM matches better with the 3-D RTM result especially in the aspect of phase congruency. Therefore, we conclude that this data conversion filter is necessary for 2-D RTM. We also conducted a laboratory experiment on a volcanic ash pit using a multiinput–multioutput GPR system, which is adopted on the Chang-E 5 lunar exploration lander and works in a stationary mode. The 3-D-to-2-D data conversion filter is applied to the measured multioffset GPR data before the 2-D RTM. The imaging results demonstrate that three marble slabs buried at different depths up to 2 m are clearly imaged.

In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR

At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I2EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm.

Permittivity Characterization of Unbound Granular Pavement Materials with a Modified Free-Space Approach

Moisture has a considerable influence on the structural performance of unbound granular (UBG) pavements. Multioffset ground-penetrating radar has the potential to quantify moisture within these materials by measuring the permittivity of pavement layers. To enable moisture estimates from these field measurements, relationships between permittivity and volumetric moisture are required. This paper describes the use of a modified free-space (MFS) laboratory approach to measure the permittivity of compacted UBG material samples and to develop these relationships. Material samples were compacted within form ply boxes; various density and moisture conditions were targeted. A vector network analyzer was used to measure the phase shift between a fixed pair of ground-coupled dipole antennas as a result of sample insertion and an initial ply sheet reference measurement. Frequency-dependent permittivity values for the samples were then determined over the range of 1.0 to 2.0 GHz on the basis of these measurements. Mean values over this range were then related to the volumetric moisture content of the samples. An indicative moisture–permittivity relationship was proposed on the basis of MFS measurements of samples from several quarries. Overall, the permittivity results from the MFS approach showed reasonably good agreement for samples at higher moisture contents compared with relationships in the literature that are based on time domain reflectometry. However, for drier samples, the MFS permittivity values were higher than the literature predictions. Possible reasons for these differences are discussed, and an overview of the advantages and limitations of using the technique for characterizing UBG materials is given.

Multi-offset ground-penetrating radar imaging of a lab-scale infiltration test

Abstract. A lab scale infiltration experiment was conducted in a sand tank to evaluate the use of time-lapse multi-offset ground-penetrating radar (GPR) data for monitoring dynamic hydrologic events in the vadose zone. Sets of 21 GPR traces at offsets between 0.44–0.9 m were recorded every 30 s during a 3 h infiltration experiment to produce a data cube that can be viewed as multi-offset gathers at unique times or common offset images, tracking changes in arrivals through time. Specifically, we investigated whether this data can be used to estimate changes in average soil water content during wetting and drying and to track the migration of the wetting front during an infiltration event. For the first problem we found that normal-moveout (NMO) analysis of the GPR reflection from the bottom of the sand layer provided water content estimates ranging between 0.10–0.30 volumetric water content, which underestimated the value determined by depth averaging a vertical array of six moisture probes by 0.03–0.05 volumetric water content. Relative errors in the estimated depth to the bottom of the 0.6 m thick sand layer were typically on the order of 2%, though increased as high as 25% as the wetting front approached the bottom of the tank. NMO analysis of the wetting front reflection during the infiltration event generally underestimated the depth of the front with discrepancies between GPR and moisture probe estimates approaching 0.15 m. The analysis also resulted in underestimates of water content in the wetted zone on the order of 0.06 volumetric water content and a wetting front velocity equal to about half the rate inferred from the probe measurements. In a parallel modeling effort we found that HYDRUS-1D also underestimates the observed average tank water content determined from the probes by approximately 0.01–0.03 volumetric water content, despite the fact that the model was calibrated to the probe data. This error suggests that the assumed conceptual model of laterally uniform, one-dimensional vertical flow in a homogenous material may not be fully appropriate for the experiment. Full-waveform modeling and subsequent NMO analysis of the simulated GPR response resulted in water content errors on the order of 0.01–0.03 volumetric water content, which are roughly 30–50% of the discrepancy between GPR and probe results observed in the experiment. The model shows that interference between wave arrivals affects data interpretation and the estimation of traveltimes. This is an important source of error in the NMO analysis, but it does not fully account for the discrepancies between GPR and the moisture probes observed in the experiment. The remaining discrepancy may be related to conceptual errors underlying the GPR analysis, such as the assumption of uniform one-dimensional flow, a lack of a sharply defined wetting front in the experiment, and errors in the petrophysical model used to convert dielectric constant to water content.

Higher-Resolution Determination of Zero-Offset Common-Reflection-Surface Stack Parameters

We developed a higher resolution method for the estimation of the three travel-time parameters that are used in the 2D zero-offset, Common-Reflection-Surface stack method. The underlying principle in this method is to replace the coherency measure performed using semblance with that of MUSIC (multiple signal classification) pseudospectrum that utilizes theeigenstructureof the data covariance matrix. The performance of the two parameter estimation techniques (i.e., semblance and MUSIC) was investigated using both synthetic seismic diffraction and reflection data corrupted with white Gaussian noise, as well as a multioffset ground penetrating radar (GPR) field data set. The estimated parameters employing MUSIC were shown to be superior of those from semblance.

Multi‐offset ground penetrating radar methods to image buried foundations of a medieval town wall, Great Yarmouth, UK

AbstractIn conventional common‐offset (CO) usage, ground‐penetrating radar (GPR) methods may be problematic where a target is hosted within a structurally complex subsurface, or where coherent noise energy obscures the response to that target. This often occurs when imaging archaeology in urban environments, where reflected and diffracted air‐wave energy can be pervasive in the presence of cars, walls and other nearby features. To overcome such issues we show a multi‐offset (MO) GPR approach to urban surveying. Multi‐offset velocity analysis is used as a filter by which ground‐propagating signal energy is separated from airborne arrivals, with the latter suppressed on application of normal moveout corrections and stacking. Pre‐stack migration is applied and yields considerable improvement to target resolution compared with post‐stack routines. The MO‐derived model of GPR velocity can also show the subsurface distribution of archaeological layering. Methods are demonstrated for a 25‐fold MO GPR profile acquired, using unshielded antennas of 200 MHz centre‐frequency, over foundations of the medieval town wall of Great Yarmouth, UK. The buried foundations represent a vertical discontinuity in the ground, which truncates both natural geological and archaeologically prospective layers. In addition to this complex geometry, which itself hinders GPR imaging, coherent air‐wave noise is problematic because there are numerous above‐surface features (e.g. parked cars, above‐surface walls) in the immediate vicinity of the profile. The improvement offered by MO techniques is benchmarked against a conventional CO routine and compared with co‐located borehole records for a comprehensive subsurface interpretation. Target foundations appear ca. 1.5 m beneath the ground surface, and GPR data support artefactual evidence of the construction of a sixteenth century rampart on their interior side. Despite the increased survey effort, we recommend MO methods at any location where CO data are dominated with coherent noise energy, and where complex subsurface geometries degrade the output from standard migration algorithms. Copyright © 2010 John Wiley & Sons, Ltd.

Estimating 3D variation in active-layer thickness beneath arctic streams using ground-penetrating radar

We acquired three-dimensional (3D) ground-penetrating radar (GPR) data across three stream sites on the North Slope, AK, in August 2005, to investigate the dependence of thaw depth on channel morphology. Data were migrated with mean velocities derived from multi-offset GPR profiles collected across a stream section within each of the 3D survey areas. GPR data interpretations from the alluvial-lined stream site illustrate greater thaw depths beneath riffle and gravel bar features relative to neighboring pool features. The peat-lined stream sites indicate the opposite; greater thaw depths beneath pools and shallower thaw beneath the connecting runs. Results provide detailed 3D geometry of active-layer thaw depths that can support hydrological studies seeking to quantify transport and biogeochemical processes that occur within the hyporheic zone.

Comparative Analysis of the Multi-Offset Ground-Penetrating Radar and Shear-Wave Reflection Methods

Ground penetrating radar and shear-wave reflection methods were used to characterize the shallow subsurface. Both methods were compared at a site in south western Sweden. The site, which consists of a [Formula: see text] long profile, was chosen based on existing site information from previous studies. The shallow subsurface consists mainly of sediments of sand and clay. Ground penetrating radar methods, using common-offset and multi-offset techniques, in combination with common midpoint processing, were compared regarding resolution and depth penetration. It is shown that the latter strategy provides more distinct reflections and allows a deeper range of interpretable reflectors. The shear-wave reflection method images overlapping and deeper parts of the section which are below the depth range resolved by the radar waves. The study shows that by combining information from ground penetrating radar and shear-wave reflection surveys, a more complete analysis of subsurface geology can be conducted. Also in conductive environments, the shear-wave reflection method offers a possible alternative to ground-penetrating radar.

Multi-offset ground penetrating radar data for improved imaging in areas of lateral complexity — Application at a Native American site

Multi-offset ground penetrating radar (GPR) data offers numerous imaging benefits over common offset GPR data, such as increased signal to noise ratio and depth penetration, and improved reflector continuity and dip imaging. Another relevant benefit of multi-offset data is the improved lateral imaging capabilities it provides. Neglecting lateral complexity considerations can produce an erroneous interpretation. An example from a Native American archaeological site highlights how 200 MHz multi-offset data provides a better estimate of the lateral density of possible archaeological material, as well as more accurate depth estimates. This is compared to 200 MHz common offset data, which produced an erroneous interpretation both in terms of potential archaeological material density and depth. This analysis also highlights an important caveat of multi-offset acquisition, which is having suitable near-surface conditions to realize improved data quality. Common offset reflectivity is very sensitive to changes in water saturation, and multi-offset data even more so due to the additional dependence of reflectivity on the multiple angles of investigation. A multi-offset section at the same location, but acquired under drier conditions, produced little imaging improvement over the common offset section. Even given the imaging benefits of multi-offset data, it is seldom acquired mainly due to the tedious task of single channel acquisition with bistatic GPR systems. Acquiring the multi-offset traces continuously as common source gathers while still maintaining positioning accuracy and signal quality halved the acquisition time of this multi-offset dataset.