Three-dimensional frequency-domain electromagnetic induction (FD-EMI) surveying involves using a profile spacing that is smaller than the crossline footprint of the measurements. Using portable multiconfiguration loop-loop sensors, this approach can image the spatial distribution of electrical conductivity in the shallow subsurface with a relatively high lateral resolution. A pseudo-3D sharp inversion procedure (spatially constrained 1D inversion) can be improved by using further constraints, such as structural prior information obtained from 3D ground-penetrating radar surveys. Two field examples indicate how structural and model sharpness constraints yield improved pseudo-3D models compared with models obtained from more classical approaches (e.g., nonstructurally constrained and based on smoothness constraints). These results also highlight the nonuniqueness of the resulting FD-EMI conductivity models, which can critically influence subsequent interpretations in terms of geologic units and petrophysical properties.

Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote- and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.

This paper presents a GIS-based integration of a multidisciplinary dataset concerning a heavy metal contaminated soil. The case study is an area extending for about half a hectare where nearly 600 young trees have been planted to carry out a phytostabilization treatment. Three different plant species have been used for the experiment, lentisk, poplar and oleander, with different characteristics in terms of foliage and mean heights. The area has been monitored by exploiting geomatics, geophysical and chemical techniques, to acquire a comprehensive dataset able to describe the vegetation health status and the soil pollution levels. Low-cost UAV flights, GNSS surveys, and geophysical and chemical investigations have been performed in the area at four different epochs. The different acquisitions were elaborated resulting in specific products (point clouds, DSMs, specific maps) and the related quantitative and qualitative parameters. All the available data have been managed in a GIS project to provide a broad description of the site and to ease the understanding of complex dynamics. The presented analysis belongs to a wider project, aiming at defining monitoring procedures for soils contaminated by heavy metals, using a combination of geomatics, geophysical, and chemical derived parameters. The final purpose will be the definition of algorithms implemented in the GIS environment, specific for different soils, plant species and microbial populations allowing the identification of the optimal conditions for the contaminated site's restoration.

Frequency-domain electromagnetic induction (EMI) methods are commonly used to map vast areas quickly and with minimum logistical efforts. Unfortunately, they are often characterized by a very limited number of frequencies and severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) approaches are usually considered more reliable; for example, they do not require specific calibration procedures and can be easily inverted in 2D/3D. However, ERT surveys are, by far, more demanding and time consuming, allowing for the deployment of a few acquisition lines per day. Ideally, the optimal would be to have the advantages of both approaches: ease of acquisition while keeping robustness and reliability. The present work raises from the necessity to cope with this issue and from the importance of enforcing realistic constraints to the data inversion without being limited to (over)simplistic spatial constraints (for example, characterizing the smooth and/or sharp regularization). Accordingly, the present research demonstrates, by means of synthetic and field data, how the EMI inversion—based on realistic prior models—can be further enhanced by incorporating additional pre-existing pieces of information. While the proposed scheme is quite general, in the specific examples discussed here, these additional pieces of information are, respectively, a reference model along a line across the survey area, and an ERT section. The field EMI results were verified against extensive ground penetrating radar (GPR) measurements and boreholes.

<p>In geophysical data inversion, one way to decrease the non-uniqueness of the solutions is to incorporate structural constraints. Such structural constraints are typically derived from collocated geophysical data, which are more sensitive to subsurface structures and parameter contrasts than the to-be-inverted data. When using a smooth regularization operator, a straightforward approach is to reduce the local weight of the smoothness constraints in model regions where we expect an interface. However, when using such an inversion approach, the capability to reconstruct a sharp interface relies only on the structural a priori information; i.e., model areas where no structural a priori information is available are solely controlled by the standard smoothness constraints. Therefore, this approach is not optimal in practice, as the structural a priori information is often not complete.</p><p>In this study, we evaluate a structurally-constrained inversion approach based on the Minimum Gradient Support (MGS) regularization, which is capable to promote sharp interfaces also in areas where no structural a priori information is explicitly specified. We test and evaluate this regularization approach for the inversion of frequency-domain electromagnetic induction (FD-EMI) data, where we use a constant-offset 3D GPR data set to derive structural a priori information. Our field data set covers an area of about 120 m x 50 m and has been collected at a field site in Kremmen, Germany, to explore peat deposits. Our results demonstrate that the proposed structurally-constrained inversion approach helps in finding a reliable subsurface structures (e.g., peat thickness) as well as a reliable reconstruction of the subsurface electrical conductivity distribution within the peat formation (e.g., related to varying degrees of peat decomposition) and within the sandy substratum.</p>

Electromagnetic induction (EMI) methods are often used to map rapidly large areas with minimal logistical efforts. However, they are limited by a small number of frequencies and by their severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) results are generally considered more reliable, with no need for specific calibration procedures and easy 2D/3D inversion. Still, ERT surveys are definitely more time-consuming, and, ideally, an approach with the advantages of both EMI and ERT would be optimal. The present research addresses this issue by incorporating realistic constraints into EMI inversion, going beyond simplistic spatial constraints like smooth or sharp regularization terms, while taking into consideration the ancillary information already available about the investigated site. We demonstrate how additional pre-existing information, such as a reference model (i.e., an existing ERT section) can enhance the EMI inversion. The study verifies the results against observations from boreholes.

Many geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a prioriinformation is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally constrained smooth inversion as well as to more standard (i.e., not structurally constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach.

We have developed a 1D laterally constrained inversion of surface-wave dispersion curves based on the minimum gradient support regularization, which allows solutions with tunable sharpness in the vertical and horizontal directions. The forward modeling consists of a finite-elements approach incorporated in a flexible nonparametric gradient-based inversion scheme, which has already demonstrated good stability and convergence capabilities when tested on other kinds of data. Our deterministic inversion procedure is performed in the shear-wave velocity log space as we noticed that the associated Jacobian indicates a reduced model dependency, and this, in turn, decreases the risks of local nonconvexity. We show several synthetics and one field example to demonstrate the effectiveness and the applicability of the proposed approach.

Frequency-domain electromagnetic (FDEM) data are commonly inverted to characterize subsurface geoelectrical properties using smoothness constraints in 1D inversion schemes assuming a layered medium. Smoothness constraints are suitable for imaging gradual transitions of subsurface geoelectrical properties caused, for example, by varying sand, clay, or fluid content. However, such inversion approaches are limited in characterizing sharp interfaces. Alternative regularizations based on the minimum gradient support (MGS) stabilizers can, instead, be used to promote results with different levels of smoothness/sharpness selected by simply acting on the so-called focusing parameter. The MGS regularization has been implemented for different kinds of geophysical data inversion strategies. However, concerning FDEM data, the MGS regularization has only been implemented for vertically constrained inversion (VCI) approaches but not for laterally constrained inversion (LCI) approaches. We present a novel LCI approach for FDEM data using the MGS regularization for the vertical and lateral direction. Using synthetic and field data examples, we demonstrate that our approach can efficiently and automatically provide a set of model solutions characterized by different levels of sharpness and variable lateral consistencies. In terms of data misfit, the obtained set of solutions contains equivalent models allowing us also to investigate the non-uniqueness of FDEM data inversion.

Distributed Acoustic Sensing (DAS) is a recent technology that acquires acoustic vibrations via fiber optics sensors. The utilization of such technique for near-surface geotechnical applications has great potential, especially for the characterization and verification of artificially stabilized ground.A popular procedure to stabilize the superficial ground (for example, for the preparation of infrastructure subgrade) is the blend of the natural shallower layer with a binder (lime and/or cement). Quality control is required when the binder hardens, and acoustic surveys are an option for non-invasive and non-destructive testing. Relevant parameters to validate the effectiveness of the stabilization procedure are the mechanical properties of the materials. The distribution of shear-wave velocities in the ground is a critical parameter for the geotechnical characterization, since it depends directly on the shear-modulus of the media.The present experiment verifies the applicability of DAS technology in such geotechnical contexts, which can be representative of a wide range of utilizations, spanning, for example, from road and pavement design to building constructions. The discussed test focuses on the spectral content of the acquired signal and on the estimation of the shear-wave distribution, and compares the DAS responses against signals measured during more traditional seismic surveys using standard geophones.Despite the inevitable differences between the datasets collected with the different techniques, all the reconstructed shear-wave velocity profiles effectively identify the stabilized soil layer. Also for this reason, one of the main conclusions is that, for geotechnical characterizations, DAS can be a convenient non-invasive alternative to more standard approaches.

The analysis of surface wave dispersion curves is a way to infer the vertical distribution of shear-wave velocity. The range of applicability is extremely wide going, for example, from seismological studies to geotechnical characterizations and exploration geophysics. However, the inversion of the dispersion curves is severely ill-posed and only limited efforts have been put into the development of effective regularization strategies. In particular, relatively simple smoothing regularization terms are commonly used, even when this is in contrast with the expected features of the investigated targets. To tackle this problem, stochastic approaches can be utilized, but they are too computationally expensive to be practical, at least, in the case of large surveys. Instead, within a deterministic framework, we evaluate the applicability of a regularizer capable of providing reconstructions characterized by tunable levels of sparsity. This adjustable stabilizer is based on the minimum support regularization, applied before on other kinds of geophysical measurements, but never on surface wave data. We demonstrate the effectiveness of this stabilizer on i) two benchmark - publicly available - datasets at crustal and near-surface scales, ii) an experimental dataset collected on a well-characterized site. In addition, we discuss a possible strategy for the estimation of the depth of investigation. This strategy relies on the integrated sensitivity kernel used for the inversion and calculated for each individual propagation mode. Moreover, we discuss the reliability, and possible caveats, of the direct interpretation of this particular estimation of the depth of investigation, especially in the presence of sharp boundary reconstructions.

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

Abstract. Reprocessing of regional-scale airborne electromagnetic data is used to build a 3D geological model of the Nasia sub-basin, northern Ghana. The resulting 3D geological model consistently integrates all the prior pieces of information brought by electromagnetic data, lithologic logs, ground-based geophysical surveys, and geological knowledge of the terrain. The geo-modeling process is aimed at defining the lithostratigraphy of the area, chiefly to improve the stratigraphic definition of the area, and for hydrogeological purposes. The airborne electromagnetic measurements, consisting of GEOTEM B-field data, were originally collected for mineral exploration purposes. Thus, those B-field data had to be (re)processed and properly inverted as the original survey and data handling were designed for the detection of potential mineral targets and not for detailed geological mapping. These new geophysical inversion results, compared with the original conductivity–depth images, provided a significantly different picture of the subsurface. The new geophysical model led to new interpretations of the geological settings and to the construction of a comprehensive 3D geo-model of the basin. In this respect, the evidence of a hitherto unexposed system of paleovalleys could be inferred from the airborne data. The stratigraphic position of these paleovalleys suggests a distinctly different glaciation history from the known Marinoan events, commonly associated with the Kodjari formation of the Voltaian sedimentary basin. Indeed, the presence of the paleovalleys within the Panabako may be correlated with mountain glaciation within the Sturtian age, though no unequivocal glaciogenic strata have yet been identified. Pre-Marinoan glaciation is recorded in rocks of the Wassangara group of the Taoudéni Basin. The combination of the Marinoan and, possibly, Sturtian glaciation episodes, both of the Cryogenian period, can be an indication of a Neoproterozoic Snowball Earth. Hence, the occurrence of those geological features not only has important socioeconomic consequences – as the paleovalleys can act as reservoirs for groundwater – but also from a scientific point of view, they could be extremely relevant as their presence would require a revision of the present stratigraphy of the area.

The 1D layered inversion of surface wave dispersion data is a powerful tool to characterize the vertical distribution of S-wave velocity. Its applications span from seismology to geotechnical engineering, going through exploration geophysics. As many others, also this non-linear inverse problem is considerably ill-posed. Thus, in the Tikhonov’s regularization framework, the associated non-uniqueness and instability of the solution with respect to the data and their uncertainty can be tackled by including prior information in the inversion process. However, for the case of the gradient-based deterministic inversion problem, only constraints enforcing smooth spatial variations of the S-velocities have been used, even when blocky targets were expected. This, clearly, generates results that might fit the observed data, but that are often not compatible with other sources of information. On the other hand, probabilistic approaches can be used to properly map the model space; however, they are still very computationally expensive to be used routinely, or to be easily integrated in a multi-physical inversion procedure involving other geophysical methods. Our goal is to combine computer efficiency, capability of integration with other geophysical methods, and some exhaustiveness regarding the non-uniqueness of the inverse problem. For this, we developed a coherent set of tools for the deterministic inversion of dispersion curves that is capable of applying a quite large spectrum of constraints. This includes, for example, vertically and laterally constrained inversions with different levels and kinds of regularization (sharpness and/or smoothness). In this study, we evaluate the capabilities and the possible limitations of the different regularization approaches on various datasets.

This research deals with the essential steps carried out during the processing and inversion of the airborne time-domain electromagnetic (TEM) data used within the framework of the GhanAqua project – aiming at the groundwater development for sustainable agriculture in the White Volta basin in Ghana.

Noninvasive methods for the characterization of shallow subsurface have been used routinely for some 20–30 years. The growth in these methods has been driven by a variety of breakthroughs in the use of electrical, electromagnetic, and seismic methods, to mention only the most common techniques. Increasing field capabilities and computational power have yet to yield all their potential fruits. In this chapter, we introduce readers to the basic concepts of shallow subsurface methods. We guide them through some of the physical details and present a number of application examples all derived from our own experience, concerning both structural characterization and (fluid)-dynamic understanding of the shallow subsurface. Finally, we propose ideas concerning the future development of this wide and exciting discipline.

The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.

This paper describes the preliminary results of integrated non-destructive surveys for the diagnosis of the materials and for the analysis of the underground structures of an historical building. The studied structure was built in the center of Cagliari, Italy. A single channel 200 MHz Ground Penetrating Radar (GPR) survey was carried out in order to provide the 3D reconstruction of the buried structures localized under the floor level of two rooms of the structure. Two 3D models of the underground environments were derived from orthogonal radar profiles. In addition, active Infrared Thermography (IRT) and Multispectral Imaging techniques (MSI) were utilized to perform the noninvasive inspection of the conditions of the surface materials. IRT images were processed via the Principal Component Analysis (PCA) technique. The reflective patterns of the GPR maps allowed to locate several buried anomalies. IRT data and MSI images have provided a fundamental support to enhance discontinuities and defects of the investigated surfaces.

Historical architecture is a primary element containing the identity values of a society. The wide diffusion of many ancient buildings gathering part of these values on painting walls over territories often characterized by poor technological or economic resources brings to consider the development of low-cost protocols to inspect valued surfaces and to give the authorities in charge of preservation and restoration adequate technical information. Here we present the preliminary results of a recent application of remote sensing micro-geophysical techniques to typical architectural targets such as vaults. A modified commercial Digital Single-Lens Reflex (DSLR) camera was used to acquire multispectral datasets on portions of a painted vault. Multispectral datasets were used raw or after the application of a pre-processing step with a Multi Images Stacking (MIS) algorithm. Multispectral images were then processed with spatial wavelet decomposition, histogram enhancing, thresholds application, image fusion, false colors compositing and Principal Component Analysis (PCA) techniques. Software used have been GNU Image Manipulation Program (GIMP) and Mathworks MATLAB (which can be substituted for the processing steps proposed by the built-in functions of GNU OCTAVE open-source software). Processed images were able to highlight features on vault paintings revealing details of the surface or its very shallow layers which were impossible or very difficult to distinguish in raw data. In fact, they emphasized low-visible details, differences in apparently similar finishes or pigments, cracks and probably details of surface preparation.