Conventional ray-based techniques for analyzing common-midpoint (CMP) ground-penetrating radar (GPR) data use part of the measured data and simplified approximations of the reality to return qualitative results with limited spatial resolution. Whereas these methods can give reliable values for the permittivity of the subsurface by employing only the phase information, the far-field approximations used to estimate the conductivity of the ground are not valid for near-surface on-ground GPR, such that the estimated conductivity values are not representative for the area of investigation. Full-waveform inversion overcomes these limitations by using an accurate forward modeling and inverts significant parts of the measured data to return reliable quantitative estimates of permittivity and conductivity. Here, we developed a full-waveform inversion scheme that uses a 3D frequency-domain solution of Maxwell’s equations for a horizontally layered subsurface. Although a straightforward full-waveform inversion is relatively independent of the permittivity starting model, inaccuracies in the conductivity starting model result in erroneous effective wavelet amplitudes and therefore in erroneous inversion results, because the conductivity and wavelet amplitudes are coupled. Therefore, the permittivity and conductivity are updated together with the phase and the amplitude of the source wavelet with a gradient-free optimization approach. This novel full-waveform inversion is applied to synthetic and measured CMP data. In the case of synthetic single layered and waveguide data, where the starting model differs significantly from the true model parameter, we were able to reconstruct the obtained model properties and the effective source wavelet. For measured waveguide data, different starting values returned the same wavelet and quantitative permittivities and conductivities. This novel approach enables the quantitative estimation of permittivity and conductivity for the same sensing volume and enables an improved characterization for a wide range of applications.
Abstract Detailed knowledge of the intra‐field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture. We present an approach that combines ground‐based agrogeophysical soil and aerial crop data to delineate field‐specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato ( Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone‐based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI–NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field‐scale EMI data included spatial soil information of the upper 0–50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI–NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high‐resolution soil information is essential for variable rate applications and agronomic modeling.
Dispersion of ground-penetrating radar (GPR) waves can occur when they are trapped in a layer. In this paper, we analyze the modal propagation of GPR pulses through a layer of ice that is overlying water. Dispersed transverse electric (TE) waves that are trapped in the waveguide have larger amplitudes than the critically refracted waves that travel through air, whereas the transverse magnetic (TM) critically refracted waves traveling through air are more dominant than the trapped dispersed TM waves. This can be explained by the leaky waveguide behavior of the ice layer. The reflection coefficients for the waves incident on the ice-water interface show that the TM modes are more leaky than the TE modes. Still, clear dispersion is observed in both cases, which depends on the permittivity and thickness of the ice. Similar to inversion of dispersed Rayleigh waves, these parameters can be estimated by calculating phase-velocity spectra, picking dispersion curves, and inverting the dispersion curves using a combined local and global minimization procedure. Synthetic data show several higher order modes of which separate and combined inversions return the input modeling parameters accurately. Experimental data acquired on a frozen lake show strong dispersion for the TE and TM modes. The phase-velocity spectra of the field data show three TE and four TM modes of which separate and combined inversion of different modes return similar values for the ice thickness and known permittivity of ice. Due to the more leaky behavior of the TM modes, the TE inversion is better constrained and more suitable for inversion.
Apparent resistivity is a useful concept for initial quickscan interpretation and quality checks in the field, because it represents the resistivity properties of the subsurface better than the raw data. For frequency‐domain soundings several apparent‐resistivity definitions exist. One definition uses an asymptote for the field of a magnetic dipole in a homogeneous half‐space and is useful only for low induction numbers. Another definition uses only the amplitude information of the total magnetic field, although this results in a non‐unique apparent resistivity. To overcome this non‐uniqueness, a complex derivation using two different source–receiver configurations and several magnetic field values for different frequencies or different offsets is derived in another definition. Using the latter theory, in practice, this means that a wide range of measurements have to be carried out, while commercial systems are not able to measure this wide range. In this paper, an apparent‐resistivity concept is applied beyond the low‐induction zone, for which the use of different source–receiver configurations is not needed. This apparent‐resistivity concept was formerly used to interpret the electromagnetic transients that are associated with the turn‐off of the transmitter current. The concept uses both amplitude and phase information and can be applied for a wide range of frequencies and offsets, resulting in a unique apparent resistivity for each individual (offset, frequency) combination. It is based on the projection of the electromagnetic field data on to the curve of the field of a magnetic dipole on a homogeneous half‐space and implemented using a non‐linear optimization scheme. This results in a fast and efficient estimation of apparent resistivity versus frequency or offset for electromagnetic sounding, and also gives a new perspective on electromagnetic profiling. Numerical results and two case studies are presented. In each case study the results are found to be comparable with those from other existing exploration systems, such as EM31 and EM34. They are obtained with a slight increase of effort in the field but contain more information, especially about the vertical resistivity distribution of the subsurface.
Due to the recent developments of multi-configuration EMI systems consisting of transmitter and multiple receivers in a portable rigid boom, detailed large-scale characterization of the top-and subsoil is nowadays possible.Using Transmitter-Receiver separations ranging from 0.3 m up to 4 m, multiple apparent electrical conductivity (ECa) values are measured for different but overlapping investigation volumes.The measured data can be used in a qualitative way to investigate the spatial ECa patterns∕clusters to determine the best locations for soil sampling.Since changes in ECa can be caused by many factors including soil water content, texture, and salinity changes, soil samples need to be used to determine the different topand subsoil properties that are responsible for the ECa contrasts.In this way, the obtained soil properties can be extrapolated into the obtained clusters resulting in a large-scale top-and subsoil characterisation over several hectares for every square meter.In order to enable a more quantitative use of the data and to obtain a reliable model of the electrical conductivity changes with depth, a calibration of the EMI measurements is required, which can be achieved using soil sampling, independent electrical resistivity tomography (ERT), or vertical electrical sounding (VES) measurements.Here, we give an overview of several agricultural applications, calibration approaches to obtain quantitative ECa values, and inversion results to obtain a quasi-3D image of the top-and subsoil.Especially the subsoil patterns were often responsible for the observed patterns in leaf area index (LAI) and airborne hyperspectral plant performance data.
Reliable high-resolution 3-D characterization of aquifers helps to improve our understanding of flow and transport processes when small-scale structures have a strong influence. Crosshole ground penetrating radar (GPR) is a powerful tool for characterizing aquifers due to the method's high-resolution and sensitivity to porosity and soil water content. Recently, a novel GPR full-waveform inversion algorithm was introduced, which is here applied and used for 3-D characterization by inverting six crosshole GPR cross-sections collected between four wells arranged in a square configuration close to the Thur River in Switzerland. The inversion results in the saturated part of this gravel aquifer reveals a significant improvement in resolution for the dielectric permittivity and electrical conductivity images compared to ray-based methods. Consistent structures where acquisition planes intersect indicate the robustness of the inversion process. A decimetre-scale layer with high dielectric permittivity was revealed at a depth of 5–6 m in all six cross-sections analysed here, and a less prominent zone with high dielectric permittivity was found at a depth of 7.5–9 m. These high-permittivity layers act as low-velocity waveguides and they are interpreted as high-porosity layers and possible zones of preferential flow. Porosity estimates from the permittivity models agree well with estimates from Neutron–Neutron logging data at the intersecting diagonal planes. Moreover, estimates of hydraulic permeability based on flowmeter logs confirm the presence of zones of preferential flow in these depth intervals. A detailed analysis of the measured data for transmitters located within the waveguides, revealed increased trace energy due to late-arrival elongated wave trains, which were observed for receiver positions straddling this zone. For the same receiver positions within the waveguide, a distinct minimum in the trace energy was visible when the transmitter was located outside the waveguide. A novel amplitude analysis was proposed to explore these maxima and minima of the trace energy. Laterally continuous low-velocity waveguides and their boundaries were identified in the measured data alone. In contrast to the full-waveform inversion, this method follows a simple workflow and needs no detailed and time consuming processing or inversion of the data. Comparison with the full-waveform inversion results confirmed the presence of the waveguides illustrating that full-waveform inversion return reliable results at the highest resolution currently possible at these scales. We envision that full-waveform inversion of GPR data will play an important role in a wide range of geological, hydrological, glacial and periglacial studies in the critical zone.
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