Multi-offset Ground-penetrating Radar Data 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).

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.

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.

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.

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.

Non Destructive Geophysical Monitoring of Water Content and Fluid Conductivity Anomalies in the Near Surface at the Border of an Agricultural

We use in this paper advanced geophysical techniques for the characterization and monitoring of subsurface properties such as porosity, water content and electrical conductivity of water. Ground Penetrating Radar (GPR) and electrical conductivity measurements were recorded monthly during one year at the border of a corn field. Velocity analyses of multioffset GPR data were conducted to determine total porosity and to monitor vertical transport of water from the soil surface to the water table. The use of novel and original techniques for GPR processing (GPR velocity estimation by the Common Reflection Surface (CRS) method, kriging applied to GPR velocity) improved the estimate and the resolution of GPR velocity maps compared with the classical Normal MoveOut (NMO) and the bi-linear interpolation. Electrical resistivities were used to determine the effective porosity. The combination of GPR and electrical data permitted to estimate the electrical conductivity of water and to highlight high conductivity zones, possibly due to contamination by agricultural fertilizers. Independent determinations (grain size fractions, electrical conductivity, major ion content of water samples and porosity) were obtained, that validate our geophysical investigation. This study demonstrates the efficiency of non destructive geophysical approaches for providing accurate models of water content, porosity and electrical conductivity of water down to a depth of several meters in a poorly conductive soil.

Velocity variations and water content estimated from multi‐offset, ground‐penetrating radar

The common midpoint (CMP) processing technique has been shown to be effective in improving the results of ground‐penetrating radar (GPR) profiling. When radar data are collected with the CMP multioffset geometry, stacking increases the signal‐to‐noise ratio of subsurface radar reflections and results in an improved subsurface image. An important aspect of CMP processing is normal‐moveout velocity analysis. Our objectives are to show the effect of multiple velocity analyses on the stacked radar image and particularly, to demonstrate that this velocity information can also be used to determine subsurface water content. Most GPR surveys are very limited in spatial extent and assume that within the survey range, radar velocity structure in the shallow subsurface can be adequately approximated by a single velocity function in data processing. In this study, we show that variation in radar velocity can be quite significant and that the stacked profile improves as the number of velocity analysis locations is increased. Interval velocities can be calculated from the normal moveout velocities derived in the CMP velocity analysis. With some reasonable assumptions about subsurface conditions necessary for radar propagation, interval velocity can be converted to an estimate of volumetric water content. Therefore, by collecting GPR data in the multioffset CMP geometry, not only is the radar profile improved but it also allows for an interpretation of subsurface variation in water content. We show the application of these techniques to multioffset GPR data from the Chalk River test area operated by Atomic Energy of Canada Limited.