Ground-penetrating Radar Velocity Research Articles

Geophysical and chemical characteristics of peatland in coastal wetland, southern Thailand

Peatlands, as an important carbon sink, store a large amount of atmospheric carbon that is vulnerable to becoming a carbon emission source due to the disturbance of the peat layer by natural and anthropogenic processes. The Kuan Kreng peat swamp forest is the second largest peatland in Thailand and is in coastal wetland serving for carbon stock in the subsurface. To assess carbon stock in peatland, the thickness and distribution of the peat layer are necessary. This study determines physical and chemical characteristics of the peatlands in the coastal wetland, southern Thailand by conventional core study and geophysical surveys, including ground penetrating radar (GPR) and electrical resistivity imaging (ERI). There is a significant relationship between chemical and physical properties from the peat layer, which is useful to approximately predict the physical properties and peat layers in the geophysical profiles. The resistivity profiles exhibit high resistivity response, peat between 21.9 and 145.0 ohm-m, interpreted as the peat layers in the shallow subsurface. The high resolution GPR profiles provide an average GPR velocity in peat of 0.040 m/ns and a relative dielectric constant of 54.9 for the peat layers. The peat layer is characterized by a high amplitude of reflection in the GPR profiles. The thickness of the peat layers was estimated from the geophysical surveys and the drilling cores having an average thickness of 18 cm. Average values of bulk density (0.19 g/cm3) and TOC (31.18 wt. %) from the drilling core samples are used to calculate the carbon density in the peat layers giving a result of 59.24 Kg C/m3. Hence, the carbon stock at the Kuan Kreng peat swamp forest is estimated at least 7.53 megaton carbon, which is much higher than aboveground biomass carbon in this area. The innumerably high amount of carbon stock could raise awareness of peatland disturbances.

GPR velocity correction method in transversely heterogeneous media based on CMP data

AbstractDetermining ground‐penetrating radar (GPR) velocity has always been a critical problem. The GPR velocity estimation method based on common midpoint (CMP) data has been widely used because of its simplicity. However, we found that in sediment investigation and soil assessment, transversal heterogeneity is universal, which violates the basic assumption of velocity estimation through CMP data. Due to the rapid change of underground media and the limitation of the scope of some surveying areas, the CMP survey line will inevitably be selected in the area where the velocity changes laterally, making it difficult to obtain accurate velocity. To address this problem, we propose a velocity correction method. First, we determined the characteristics of CMP data and the corresponding velocity spectra acquired in transversely heterogeneous media through numerical simulations. Subsequently, we found that the simulated CMP data could determine the location of changes in the underground medium, and that the velocity obtained from the semblance analysis could be corrected according to the location where the medium changes laterally. We then used models wherein the thickness, relative permittivity and proportion of abnormal parts varied independently or simultaneously to verify the proposed velocity correction method. The results show that our method can control the GPR velocity error within 3.44%, and the precision is about 0.002 m/ns. Finally, we conducted a sediment investigation experiment on a channel bar in the lower reaches of the Yarlung Zangbo River. We determined the interface at which the sediment changed transversely and obtained the corresponding electromagnetic velocity using the proposed method. This study provides a reliable method for determining the GPR velocity in transversal heterogeneous media, which is of great significance for various practical applications.

Finetuning ground penetrating radar velocity analysis from hyperbola fitting using migration

AbstractThe widely used tool, ground penetrating radar (GPR), has proven to be an excellent research method for glaciological studies. The total ice thickness and englacial structures can be studied, and the method can give information on the temperature regime within the ice. Good velocity profiles are needed to convert measured two‐way travel time to depth information. In addition, a good velocity analysis can be used to improve data processing. A number of methods can be used to find the GPR wave velocity, which are briefly described. This study used hyperbola fitting to estimate the velocity in a glaciological study. In addition, Kirchhoff's migration was used to fine‐tune this velocity estimate. Within the Longyearbreen glacier, situated next to Longyearbyen on Spitsbergen, Svalbard archipelago, objects in the ice act as scattering points that create hyperbolas. Using the hyperbola‐fitting method, a GPR wave velocity equal to 0.170 ± 0.005 m/ns was estimated. By doing Kirchhoff's migration and studying the collapse of hyperbolas with variations of the migration velocity, a more accurate velocity estimate can be achieved; 0.172 ± 0.002 m/ns. A more accurate velocity estimate like this opens possibilities for better characterization of the ice body and ice thickness variations, as well as the variation of these as a function of time. In March 2022, the maximum ice thickness in the investigated area was calculated to be 88 ± 1 m.

Analysis of subsurface velocity using CMP gathers picked up by unshielded GPR system: results from an experimental NAPL contaminated test site

Adequate knowledge of velocity is required for accurate data imaging and depth conversion, as well as for quantifying the distribution of soil water content. Without complementary borehole information in the form of dielectric permittivity and/or porosity logs along the profile, it is currently impossible to reliably estimate the high-frequency electromagnetic velocity distribution in the probed subsurface region. Here, we present a new method for calculating the precise subsurface velocity structure from ground penetrating radar (GPR) reflection data that does not require boreholes or log data. This study investigates the ability of the pulse_EKKO PRO GPR system to predict a vertical profile for the possible velocity estimation of a layered and contaminated geophysical test site in Hangzhou, China. All data were acquired and saved on the GPR system in various files (projects) before analysis using GPR software to obtain approximated velocity modelling using common midpoint (CMP) gathers. Using the velocity spectrum analysis, a vertical profile of the interval velocities can be derived from each CMP gather. The findings of this study indicate that the proposed method is effective and sustainable. Furthermore, owing to the efficacy of the method in terms of field effort and computational complexity, it can easily be expanded to 3D GPR velocity exploration, increasing its importance in comparison to standard offset-based techniques for estimating velocity using GPR.

Drone-mounted ground-penetrating radar surveying: Flight-height considerations for diffraction-based velocity analysis

Recent studies highlight the potential of the drone platform for ground-penetrating radar (GPR) surveying. Most guidance for optimizing drone flight heights is based on maximizing the image quality of target responses, but no study yet considers the impact on diffraction traveltimes. Strong GPR velocity contrasts across the air-ground interface introduce significant refraction effects that distort diffraction hyperbolas and introduce errors into diffraction-based velocity analysis. The severity of these errors is explored with synthetic GPR responses, using ray- and finite-difference approaches, and a field GPR data set acquired over a sequence of diffracting features buried up to 1 m depth. Throughout, GPR antennas with 1000 MHz center frequency are raised from the ground to heights <0.9 m (i.e., 0–3 times the wavelength in air). Velocity estimates are within +10% of modeled values (spanning from 0.07 to 0.13 m/ns) if the antenna height is within 1/2 wavelength in air above the ground surface. Greater heights reduce diffraction curvature, damaging velocity precision, and masking diffractions against a background of subhorizontal reflectivity. Field data highlight further problems of the drone-based platform, with data dominated by reverberations in the air gap and reduced spatial resolution of wavelets at target depth. We suggest that a drone-based platform is unsuitable for diffraction-based velocity analysis, and any future drone surveys are benchmarked against ground-coupled data sets.

Integration of Geophysical Methods for Doline Hazard Assessment: A Case Study from Northern Oman

Subsurface formations with low compaction, often, due to the presence of underlying cavities, are potential sources of hazards. Thus, understanding the occurrence, properties, and extension of these weak zones poses a major concern in engineering geophysics. In this study, we examine the ability of geophysical methods to map weak areas over carbonates in Northern Oman. The weak zones are known to cause surface depression in many areas. The geophysical methods examined involve Ground-Penetrating Radar (GPR), Seismic Refraction Tomography (SRT), and Electrical Resistivity Tomography (ERT). This integrated geophysical survey was conducted near the Bimmah sinkhole, in the Quriya area, Northern Oman. The survey covers both an area with ground truth (low compaction sediments overlaying a cave) and a part with unknown subsurface properties. GPR velocity analysis using selected diffraction’s fitting helped to identify high-velocity anomalies that were attributed to the cavity. The GPR interpretation was calibrated with SRT and ERT. The former showed a clear drop in P-wave velocity and low ray coverage at the cavity zone, while the latter demonstrated high resistivity anomalies caused by the air filling the cavity. The scope was to examine the geophysical methods response, especially the GPR, and utilize the results of this preliminary approach for a wider exploration investigation in the area. The results from the study indicated that the GPR is capable to serve as a pioneer method in detecting the cavities. Hence, the GPR will cover large area in the site and the other two methods will be used as complementary for the final subsurface conditions’ evaluation.

High-resolution velocity estimation from surface-based common-offset GPR reflection data

SUMMARY Surface-based common-offset ground-penetrating radar (GPR) reflection profiling is a popular geophysical exploration technique for obtaining high-resolution images of the shallow subsurface in a cost-effective manner. One drawback of this technique is that, without complementary borehole information in form of dielectric permittivity and/or porosity logs along the profile, it is currently not possible to obtain reliable estimates of the high-frequency electromagnetic velocity distribution of the probed subsurface region. This is problematic because adequate knowledge of the velocity is needed for accurate imaging and depth conversion of the data, as well as for quantifying the distribution of soil water content. To overcome this issue, we have developed a novel methodology for estimating the detailed subsurface velocity structure from common-offset GPR reflection measurements, which does not require additional conditioning information. The proposed approach combines two key components: diffraction analysis is used to infer the smooth, large-scale component of the velocity distribution, whereas the superimposed small-scale fluctuations are inferred via inversion of the reflected wavefield. We test and validate our method on two synthetic data sets having increasing degrees of complexity and realism before applying it to a field example from the Boise Hydrogeophysical Research Site, where independent control data in the form of neutron–neutron porosity logs are available for validation. The results obtained demonstrate the viability and robustness of the proposed approach. Further, due to its efficiency, both in terms of field effort and computational cost, the method can be readily extended to 3-D, which further enhances its attractiveness compared to multi-offset-based GPR velocity estimation techniques.

Near Surface Velocity Estimation Using GPR Data: Investigations by Numerical Simulation, and Experimental Approach with AVO Response

The velocity of near-surface materials is one of the most important for Ground-Penetrating Radar (GPR). In the study, we evaluate the options for determining the GPR velocity to measure the accuracy of velocity approximations from the acquired GPR data at an experimental site in Hangzhou, China. A vertical profile of interval velocities can be estimated from each common mid-point (CMP) gather using velocity spectrum analysis. Firstly, GPR data are acquired and analyzed using the popular method of hyperbola fitting which generated surprisingly high subsurface signal velocity estimates while, for the same profile, the Amplitude variation with offset (AVO) analysis of the GPR data (using the same hyperbola fitting method) generate a more reasonable subsurface signal velocity estimate. Several necessary processing steps are applied both for CMP and AVO analysis. Furthermore, experimental analysis is conducted on the same test site to get velocities of samples based on dielectric constant measurement during the drilling process. Synthetic velocities generated by AVO analysis are validated by the experimental velocities which confirmed the suitability of velocity interpretations.

An integrated geophysical and GIS based approach improves estimation of peatland carbon stocks

Estimations of peatland carbon stocks often use generalised values for peat thickness and carbon content. Ground penetrating radar (GPR), a rapid technique for field data collection, has been increasingly demonstrated as an appropriate method of mapping peat thickness. Light Detection and Ranging (LiDAR) data as a method for understanding peatland surface elevation are also becoming more widely available. Reliable mapping and quantification of site-specific carbon stocks (e.g. upland raised bogs) is therefore, becoming increasingly feasible, providing a valuable contribution to regional, national and potentially global carbon stock assessments. This is particularly important because raised bogs, such as those found in South Wales are considerable carbon stores. They are, however, susceptible to climate warming owing to their southerly location within the UK. Accurate estimates of peatland carbon stocks has broader importance because world-wide peatland carbon stores are significant and threatened by climate change, posing a substantial challenge not only due to climate feedbacks if this stored carbon is released into the atmosphere, but also the impact on the other ecosystem services that they provide.Here, we assess the value of an integrated GPR, LiDAR and Geographic Information System (GIS) approach to improve estimation of regional carbon stocks. We apply the approach to three ombrotrophic raised bogs in South Wales, UK, selected for their conservation value and their topographically-confined raised bog form.GPR and LiDAR are found to be well suited, respectively, to mapping peat thickness at bog scale and surface elevation, thus allowing surface and basal topographies to be evaluated using GIS. In turn, this allows peat volumes to be estimated. For the first time, we record values between 55,200 m3 and 163,000 m3 for the sites considered here.The greater confidence in these peat volume estimates results from the ability to calibrate the GPR velocity using a depth-to-target calibration with peat cores extracted at locations encompassing the deepest bog area. Peat thickness is mapped at the bog scale with near centimetre precision, improving the robustness of subsequent volume calculations and our understanding of the contribution of these small but numerous sites to regional carbon stocks. Our evaluation shows that GPR corresponds well with conventional manual probing but is minimally invasive and therefore less disturbing of sensitive peatland sites, while also offering improved coverage and spatial resolution with less time and cost.In combination with measured bulk density and organic carbon contents, these peat volumes allow carbon stocks to be estimated with greater confidence compared to conventional approaches, having values between 2181 ± 122 tonnes carbon and 6305 ± 351 tonnes carbon at our three sites.

Soil Moisture Calibration Equations for Active Layer GPR Detection—a Case Study Specially for the Qinghai–Tibet Plateau Permafrost Regions

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (θ) and the soil dielectric constant (ε). The θ–ε relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai–Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai–Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as υ-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within ±0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m3/m3. While when the GPR velocity interpretation error is larger than ±0.004 m/ns, a piecewise formula calculation method, combined with the υ-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai–Tibet plateau permafrost regions.

Diffraction imaging of ground-penetrating radar data

Diffractions caused by, e.g., faults, fractures, and small-scale heterogeneity localized near the surface are often used in ground-penetrating radar (GPR) reflection studies to constrain the subsurface velocity distribution using simple hyperbola fitting. Interference with reflected energy makes the identification of diffractions difficult. We have tailored and applied a diffraction imaging method to improve imaging for surface reflection GPR data. Based on a plane-wave destruction algorithm, the method can separate reflections from diffractions. Thereby, a better identification of diffractions facilitates an improved determination of GPR wave velocities and an optimized migration result. We determined the potential of this approach using synthetic and field data, and, for the field study, we also compare the estimated velocity structure with crosshole GPR results. For the field data example, we find that the velocity structure estimated using the diffraction-based process correlates well with results from crosshole GPR velocity estimation. Such improved velocity estimation may have important implications for using surface reflection GPR to map, e.g., porosity for fully saturated media or soil moisture changes in partially saturated media because these physical properties depend on the dielectric permittivity and thereby also the GPR wave velocity.

Rapid Estimation of Volumetric Groundwater Recharge in the Vadose Zone via Ground Penetrating Radar

Saturation in the vadose zone is known to reduce the ground penetrating radar (GPR) electromagnetic wave velocities. We examine the impact of increased water content in highly-permeable beach and dune systems on GPR velocities along coastal margin of Perth, Western Australia. We acquire repeat GPR transects in May and August before and after annual high rainfall periods. The assumption of exceedingly flat water table reflector permits us to estimated change in GPR velocity at successive dates. This change in velocity can be translated to an estimated change in water saturation via the Topp relationship which is an imperial mapping of water saturation to dielectric permittivity.

Ground-Penetrating Radar Velocity Determination and Precision Estimates Using Common-Midpoint (CMP) Collection with Hand-Picking, Semblance Analysis and Cross-Correlation Analysis: A Case Study and Tutorial for Archaeologists

The most crucial parameter to be determined in an archaeological ground-penetrating radar (GPR) survey is the velocity of the subsurface material. Precision velocity estimates comprise the basis for depth estimation, topographic correction and migration, and can therefore be the difference between spurious interpretations and/or efficient GPR-guided excavation with sound archaeological interpretation of the GPR results. Here, we examine the options available for determining the GPR velocity and for assessing the precision of velocity estimates from GPR data, using data collected at a small-scale iron-working site in Rhode Island, United States. In the case study, the initial velocity analysis of common-offset GPR profile data, using the popular method of hyperbola fitting, produced some unexpectedly high subsurface signal velocity estimates, while analysis of common midpoint (CMP) GPR data yielded a more reasonable subsurface signal velocity estimate. Several reflection analysis procedures for CMP data, including hand and automated signal picking using cross-correlation and semblance analysis, are used and discussed here in terms of efficiency of processing and yielded results. The case study demonstrates that CMP data may offer more accurate and precise velocity estimates than hyperbola fitting under certain field conditions, and that semblance analysis, though faster than hand-picking or cross-correlation, offers less precision.

Geophysical imaging of the post-earthquake structural integrity of flood-control stopbanks, Christchurch, New Zealand

On 4 September 2010, a MW 7.1 earthquake struck the Canterbury region of the South Island of New Zealand. Widespread liquefaction caused major damage to many structures, including the flood-control stopbanks along the lower reaches of the Waimakariri and Kaiapoi Rivers. Additional damage occurred during the subsequent MW 6.2–6.3 earthquakes of 22 February and 13 June 2011. Repeated LiDAR surveys indicated that up to 1 m of subsidence occurred in places. Visual inspections identified areas of significant damage, which have been repaired. However, internal damage to the stopbanks cannot be recognized by visual inspection. Thus electromagnetic (EM) and ground penetrating radar (GPR) surveys were undertaken.A pilot study was completed upstream of the confluence of the two rivers, along the northeast segment of the Waimakariri stopbanks and the southwest section of the Kaiapoi stopbanks. A complementary horizontal loop EM (HLEM) survey was carried out in advance of the GPR surveys. The HLEM measurements were done with the instrument oriented both parallel and perpendicular (transverse) to the stopbanks. Anomalous HLEM responses were noted at one location; subsequent GPR surveys indicated a change in the style of stopbank construction and repair, and possibly some internal cracking. HLEM readings were also taken at high and low tide levels along the tidally-influenced lower Kaiapoi River, and significant differences were observed. Finally, during the surveys, a surface crack was observed at one location, and a GPR line across that site suggested that the crack extended to depth.The results were complemented by velocity analyses using subsurface diffractions, and velocity variations were noted along the lengths of the stopbanks. The velocity changes appear to be broadly correlated with changes in the HLEM conductivity, which is not unexpected given the effects of water content and clay content on both the electrical conductivity and the GPR velocity.

Predicting 2D geotechnical parameter fields in near-surface sedimentary environments

Abstract For a detailed characterization of near-surface environments, geophysical techniques are increasingly used to support more conventional point-based techniques such as borehole and direct-push logging. Because the underlying parameter relations are often complex, site-specific, or even poorly understood, a remaining challenging task is to link the geophysical parameter models to the actual geotechnical target parameters measured only at selected points. We propose a workflow based on nonparametric regression to establish functional relationships between jointly inverted geophysical parameters and selected geotechnical parameters as measured, for example, by different borehole and direct-push tools. To illustrate our workflow, we present field data collected to characterize a near-surface sedimentary environment. Our field data base includes crosshole ground penetrating radar (GPR), seismic P-, and S-wave data sets collected between 25 m deep boreholes penetrating sand- and gravel dominated sediments. Furthermore, different typical borehole and direct-push logs are available. We perform a global joint inversion of traveltimes extracted from the crosshole geophysical data using a recently proposed approach based on particle swarm optimization. Our inversion strategy allows for generating consistent models of GPR, P-wave, and S-wave velocities including an appraisal of uncertainties. We analyze the observed complex relationships between geophysical velocities and target parameter logs using the alternating conditional expectation (ACE) algorithm. This nonparametric statistical tool allows us to perform multivariate regression analysis without assuming a specific functional relation between the variables. We are able to explain selected target parameters such as characteristic grain size values or natural gamma activity by our inverted geophysical data and to extrapolate these parameters to the inter-borehole plane covered by our crosshole experiments. We conclude that the ACE algorithm is a powerful tool to analyze a multivariate petrophysical data base and to develop an understanding of how a multi-parameter geophysical model can be linked and translated to selected geotechnical parameters.

Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models

ABSTRACTVelocity models are essential to process two‐ and three‐dimensional ground‐penetrating radar (GPR) data. Furthermore, velocity information aids the interpretation of such data sets because velocity variations reflect important material properties such as water content. In many GPR applications, common midpoint (CMP) surveys are routinely collected to determine one‐dimensional velocity models at selected locations. To analyse CMP data gathers, spectral velocity analyses relying on the normal‐moveout (NMO) model are commonly employed. Using Dix’s formula, the derived NMO velocities can be further converted to interval velocities which are needed for processing and interpretation. Because of the inherent assumptions and limitations of such approaches, we investigate and propose an alternative procedure based on the global inversion of reflection travel‐times. We use a finite‐difference solver of the Eikonal equation to accurately solve the forward problem in combination with particle swarm optimization (PSO) to find one‐dimensional GPR velocity models explaining our data. Because PSO is a robust and efficient global optimization tool, our inversion approach includes generating an ensemble of representative solutions that allows us to analyse uncertainties in the model space. Using synthetic data examples, we test and evaluate our inversion approach to analyse CMP data collected across typical near‐surface environments. Application to a field data set recorded at a well‐constrained test site including a comparison to independent borehole and direct‐push data, further illustrates the potential of the proposed approach, which includes a straightforward and understandable appraisal of non‐uniqueness and uncertainty issues, respectively. We conclude that our methodology is a feasible and powerful tool to analyse GPR CMP data and allows practitioners and researchers to evaluate the reliability of CMP derived velocity models.

Estimating Tree-Root Biomass in Different Depths Using Ground-Penetrating Radar: Evidence from a Controlled Experiment

Roots have important functions in the ecosystem. Therefore, establishing root-related parameters such as root size, biomass, and 3-D architecture is necessary. Traditional methods for measuring tree roots are labor intensive and destructive to nature, limiting quantitative and repeated assessments in long-term research. Ground-penetrating radar (GPR) provides a nondestructive method for measuring tree roots. This study investigates the feasibility of a GPR system with 500-MHz, 900-MHz, and 2-GHz measurement frequencies for detecting tree roots and estimating root biomass under controlled experimental conditions in a sandy area. After energy attenuation correction and velocity analysis, not only the individual root in subsurface is able to be located but also the parameters that correlate well with root biomass can be extracted from the processed GPR data. The major findings were as follows. First, both the amplitude and amplitude-area indices were confirmed to be more effective for estimating root biomass after attenuation-effect compensation. This result suggests that the calibration of GPR wave-attenuation effects and velocity changes with depth are helpful in estimating root biomass from GPR parameters. Second, the selection of GPR system frequency was mainly dependent on field conditions, particularly soil water content. Lower frequency was recommended for developing root biomass estimation model under varied soil conditions. Third, the new method based on the metal reflector experiment was effective and easy to perform in situ for attenuation-effect correction.

Structure-coupled joint inversion of geophysical and hydrological data

In groundwater hydrology, geophysical imaging holds considerable promise for improving parameter estimation, due to the generally high resolution and spatial coverage of geophysical data. However, inversion of geophysical data alone cannot unveil the distribution of hydraulic conductivity. Jointly inverting geophysical and hydrological data allows us to benefit from the advantages of geophysical imaging and, at the same time, recover the hydrological parameters of interest. We have applied a coupling strategy between geophysical and hydrological models that is based on structural similarity constraints. Model combinations, for which the spatial gradients of the inferred parameter fields are not aligned in parallel, are penalized in the inversion. This structural coupling does not require introducing a potentially weak, unknown, and nonstationary petrophysical relation to link the models. The method was first tested on synthetic data sets and then applied to two combinations of geophysical/hydrological data sets from a saturated gravel aquifer in northern Switzerland. Crosshole ground-penetrating radar (GPR) traveltimes were jointly inverted with hydraulic tomography data, as well as with tracer mean arrival times, to retrieve the 2D distribution of GPR velocities and hydraulic conductivities. In the synthetic case, incorporating the GPR data through a joint inversion framework improved the resolution and localization properties of the estimated hydraulic conductivity field. For the field study, recovered hydraulic conductivities were in general agreement with flowmeter data.

Compressional and EM wave velocity anisotropy in a temperate glacier due to basal crevasses, and implications for water content estimation

AbstractWe have conducted a series of experiments designed to investigate elastic and electromagnetic (EM) velocity anisotropy associated with a preferentially aligned fracture system on a temperate valley glacier in south-central Alaska, USA. Measurements include a three-dimensional compressional wave (P-wave) seismic reflection survey conducted over a 300 m x 300 m survey patch, with uniform source grid and static checkerboard receiver pattern. Additionally, we acquired a multi-azimuth, multi-offset, polarimetric ground-penetrating radar (GPR) reflection experiment in a wagon-wheel geometry with 94° of azimuthal coverage. Results show azimuthal variation in the P-wave normal-moveout velocity of >3% (3765 and 3630 m s–1 in the fast and slow directions respectively) and difference of nearly 5% between the fast (0.164 m ns–1) and slow (0.156 m ns–1) EM velocities. Fracture orientations estimated from the GPR and seismic velocity data are consistent and indicate a preferred fracture orientation that is 30-45° oblique to glacier flow; these measurements agree with borehole observations. Anisotropic analysis of the polarimetric data gives a single volumetric water content estimate of 0.73 ±0.11%. We conclude that meaningful estimates of physical properties in glaciers based on EM or seismic velocity measurements require collecting data such that the presence of anisotropy can be evaluated and an anisotropic analysis employed when necessary.

Modeling 3D porosity and permeability from GPR data in the Ellenburger Dolomite, central Texas

Three-dimensional porosity and permeability were modeled in an Ellenburger carbonate reservoir analog from 2D crosshole and 3D surface survey ground-penetrating radar (GPR) data. Two-dimensional GPR crosshole velocity tomography, 3D migration of the GPR surface data, and porosity and permeability calibration to GPR attributes results in 3D porosity and permeability predictions that provide a consistent model of the paleocave structures and facies distributions. Picking the maximum instantaneous amplitude of the direct arrival wavelet for velocity tomography reduces uncertainties caused by a low signal-to-noise ratio, uncorrelated noise, and the interference between reflections and critical refractions at the earth/air interface. The GPR velocity is anisotropic with an average vertical to horizontal velocity ratio of 0.93, which is attributed to the dominance of the relatively horizontal orientation of the maximum porosity and permeability. Porosity and permeability trends are influenced by regional northeast-southwest and northwest-southeast striking conjugate fractures associated with the Pennsylvanian Ouachita orogeny and breccia facies generated by three episodes of burial and the resulting paleocave collapses. At depths [Formula: see text] from the present surface, large brecciated dolomite and limestone blocks have low porosity and low permeability. Between a depth of [Formula: see text] and [Formula: see text] irregular fracture orientations and distributions associated with collapse breccias have a higher average porosity and permeability. A deeper zone ([Formula: see text] to [Formula: see text] in depth), has intermediate permeability and porosity. Porosity and permeability could not be calibrated in open voids. Thus, the predictions are applicable to the core-scale to which they were calibrated, but are lower bounds for the whole volume, which contains breccias, karst, and fractures that increase both porosity and permeability at larger scales.