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Given the range of geological conditions under which airborne EM surveys are conducted, there is an expectation that the 2D and 3D methods used to extract models that are geologically meaningful would be favoured over ID inversion and transforms. We do after all deal with an Earth that constantly undergoes, faulting, intrusions, and erosive processes that yield a subsurface morphology, which is, for most parts, dissimilar to a horizontal layered earth.
We analyse data from a survey collected in the Musgrave province, South Australia. It is of particular interest since it has been used for mineral prospecting and for a regional hydro-geological assessment. The survey comprises abrupt lateral variations, more-subtle lateral continuous sedimentary sequences and filled palaeovalleys. As consequence, we deal with several geophysical targets of contrasting conductivities, varying geometries and at different depths. We invert the observations by using several algorithms characterised by the different dimensionality of the forward operator.
Inversion of airborne EM data is known to be an ill-posed problem. We can generate a variety of models that numerically adequately fit the measured data, which makes the solution non-unique. The application of different deterministic inversion codes or transforms to the same dataset can give dissimilar results, as shown in this paper. This ambiguity suggests the choice of processes and algorithms used to interpret AEM data cannot be resolved as a matter of personal choice and preference.
The degree to which models generated by a ID algorithm replicate/or not measured data, can be an indicator of the data's dimensionality, which perse does not imply that data that can be fitted with a 1D model cannot be multidimensional. On the other hand, it is crucial that codes that can generate 2D and 3D models do reproduce the measured data in order for them to be considered as a plausible solution. In the absence of ancillary information, it could be argued that the simplest model with the simplest physics might be preferred.
For near surface geophysical surveys, small-fixed offset loop-loop electromagnetic induction (EMI) sensors are usually placed parallel to the ground surface (i.e., both loops are at the same height above ground). In this study, we evaluate the potential of making measurements with a system that is not parallel to the ground; i.e., by positioning the system at different inclinations with respect to ground surface. First, we present the Maxwell theory for inclined magnetic dipoles over a homogeneous half space. By analyzing the sensitivities of such configurations, we,show that varying the angle of the system would result in improved imaging capabilities. For example, we show that acquiring data with a vertical system allows detection of a conductive body with a better lateral resolution compared to data acquired using standard horizontal configurations. The synthetic responses are presented for a heterogeneous medium and compared to field data acquired in the historical Park Sanssouci in Potsdam, Germany. After presenting a detailed sensitivity analysis and synthetic examples of such ground conductivity measurements, we suggest a new strategy of acquisition that allows to better estimate the true distribution of electrical conductivity using instruments with a fixed, small offset between the loops. This strategy is evaluated using field data collected at a well-constrained test-site in Horstwalde (Germany). Here, the target buried utility pipes are best imaged using vertical system configurations demonstrating the potential of our approach for typical applications. (C) 2015 Elsevier B.V. Pill rights reserved.
Most airborne electromagnetic (EM) processing programs assume a flat ground surface. However, in mountainous areas, the system can be at an angle with regard to the ground. As the system is no longer parallel to the ground surface, the measured magnetic field has to be corrected and the ground induced eddy current has to be modelled in a better way when performing a very fine interpretation of the data. We first recall the theoretical background for the modelling of a magnetic dipole source and study it in regard to the case of an arbitrarily oriented magnetic dipole. We show in particular how transient central loop helicopter borne data are influenced by this inclination. The result shows that the effect of topography on airborne EM is more important at early time windows and for systems using a short cut-off source. In this paper, we suggest that an estimate be made off the locally averaged inclination of the system to the ground and then to correct the data for this before inverting it (whether the inversion assumes a flat 1D, 2D or 3D sub-surface). Both 1D and 2D inversions are applied to synthetic and real data sets with such a correction. The consequence on the ground imaging is small for slopes with an angle less than 25 degrees but the correction factor can be useful for improving the estimation of depths in mountainous areas.
Information regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering.
Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes.
Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture.
Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany.
Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points.
In addition, soil analysis and classification results are available for 132 regularly distributed positions up to depths of 1.2 m.
We compare and link three-dimensional resistivity models obtained via data inversion to soil composition and soil moisture as provided by our point data.
Both the soil samples and the resistivity models indicate a two-layer medium characterized by a sandy top layer with varying thickness and a loamy bottom soil.
For all six field campaigns, we observe similar resistivity patterns reflecting the temporally stable influence of soil texture.
While the overall patterns are stable, the range of resistivity values changes with soil moisture. Finally, to estimate spatial models of soil moisture, we link our soil moisture and resistivity data using empirical petrophysical models relying on a second order polynomial function.
We observe a mean prediction error for soil moisture of +/-0.034 m3 & BULL; m? 3 using all observation points while we notice that point-specific models further reduce the error.
Thus, we conclude that our experimental and data analysis strategies represent a reliable approach to establish site-specific models and to estimate three-dimensional moisture distribution including temporal variations.
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.
Direct current systems employing a kinematic surveying strategy allow to analyze the electrical resistivity of the subsurface for large areas (i.e., several hectares). Typical applications are found in precision agriculture, archaeological prospecting and soil sciences. With the typical survey setting, the collected data sets are often characterized by a rather high level of noise and a rather coarse lateral sampling compared to data acquired with fixed electrodes. We therefore present an efficient one-dimensional inversion approach in which we put special attention on modeling the effects of noise. We apply this method to data recorded with a five-offset equatorial dipole-dipole system employing rolling electrodes. By performing several synthetic tests with realistic noise levels, we found that the considered five-configuration soundings allow for a reliable imaging of two-layer cases in the uppermost two meters of the subsurface, where the subsurface can be assumed to follow a horizontally layered geometry within 3 m around the system. By analyzing the corresponding sensitivity functions, we also show that the equatorial dipole-dipole array is relatively well suited for a 1D inversion approach compared to standard in-line electrode arrays. To illustrate this aspect, we show that our method can provide results similar to those obtained with a 2D Wenner imaging procedure for data recorded across a well-constrained 2D target. We finally apply our method to a large five-offset data set acquired in an agricultural study. The final pseudo-3D model of electrical resistivity is in accordance with borehole data available for the surveyed area. Our results demonstrate the applicability and the versatility of the presented inversion approach for large-scale data sets as they are typically collected with such rolling electrode systems. (C) 2017 Elsevier B.V. All rights reserved.
Electromagnetic induction (EMI) sensors using sufficiently low-frequency harmonic sources and sufficiently small loop separations operate in the low-induction-number (LIN) domain for a relatively wide range of background conductivity. These systems are used in diverse near-surface investigations including applications from soil sciences, hydrology, and archaeology. The special case of portable multiconfiguration EMI sensors operating at frequencies <= 20 kHz offers the possibility of using a fast linear deconvolution method to interpret multichannel data sets in three dimensions. Here, we have developed a fast 3D inversion/deconvolution method regularized with 3D smoothness constraints and formulated in the hybrid spectral-spatial domain. Compared with other linear approaches, the spectral-spatial domain formulation significantly reduces the computational cost of the processing and opens the door for real-time 3D interpretation of large data sets consisting of more than 100,000 data points. First, we test our proposed algorithm on synthetic data sets computed with the full Maxwell theory. Then, we apply our method to a real four-configuration EMI data set acquired to map the thickness of peat layers embedded in a sandy environment. For the synthetic and the field example, we compared our result with the result obtained using a standard point-by-point 1D nonlinear inversion approach. This comparison demonstrates that the proposed methodology provides superior lateral resolution compared with the 1D nonlinear inversion, at the same time significantly reducing the computational cost of the processing.
Ground loop-loop electromagnetic surveys are often conducted to fulfill the low-induction-number condition. To image the distribution of electric conductivity inside the ground, it is then necessary to collect a multioffset data set. We considered that less time-consuming constant offset measurements can also reach this objective. This can be achieved by performing multifrequency soundings, which are commonly performed for the airborne electromagnetic method. Ground multifrequency soundings have to be interpreted carefully because they contain high-induction-number data. These data are interpreted in two steps. First, the in-phase and out-of-phase data are converted into robust apparent conductivities valid for all the induction numbers. Second, the apparent conductivity data are inverted in 1D and 2D to obtain the true distribution of the ground conductivity. For the inversion, we used a general half-space Jacobian for the apparent conductivity valid for all the induction numbers. This method was applied and validated on synthetic data computed with the full Maxwell theory. The method was then applied on field data acquired in the test site of Provins, in the Parisian basin, France. The result revealed good agreement with borehole and geologic information, demonstrating the applicability of our method.
We present an algorithm that performs sequentially one-dimensional inversion of subsurface magnetic permeability and electrical conductivity by using multi-configuration electromagnetic induction sensor data. The presented method is based on the conversion of the in-phase and out-of-phase data into effective magnetic permeability and electrical conductivity of the equivalent homogeneous half-space. In the case of small-offset systems, such as portable electromagnetic induction sensors, for which in-phase and out-of-phase data are moderately coupled, the effective half-space magnetic permeability and electrical conductivity can be inverted sequentially within an iterative scheme. We test and evaluate the proposed inversion strategy using synthetic and field examples. First, we apply it to synthetic data for some highly magnetic environments. Then, the method is tested on real field data acquired in a basaltic environment to image a formation of archaeological interest. These examples demonstrate that a joint interpretation of in-phase and out-of-phase data leads to a better characterisation of the subsurface in magnetic environments such as volcanic areas.