Search Results (12)

In mineral, environmental, and engineering explorations, we frequently encounter geological bodies with varied sizes, depths, and conductivity contrasts with surround rocks and try to interpret them with single survey data. The conventional three-dimensional (3-D) inversions significantly rely on the size of the grids, which should be smaller than the smallest geological target to achieve a good recovery to anomalous electric conductivity. However, this will create a large amount of unknowns to be solved and cost significant time and memory. In this paper, we present a multi-scale (MS) stochastic inversion scheme based on shearlet transform for airborne electromagnetic (AEM) data. The shearlet possesses the features of multi-direction and multi-scale, allowing it to effectively characterize the underground conductivity distribution in the transformed domain. To address the practical implementation of the method, we use a compressed sensing method in the forward modeling and sensitivity calculation, and employ a preconditioner that accounts for both the sampling rate and gradient noise to achieve a fast stochastic 3-D inversion. By gradually updating the coefficients from the coarse to fine scales, we obtain the multi-scale information on the underground electric conductivity. The synthetic data inversion shows that the proposed MS method can better recover multiple geological bodies with different sizes and depths with less time consumption. Finally, we conduct 3-D inversions of a field dataset acquired from Byneset, Norway. The results show very good agreement with the geological information. Full article

Airborne electromagnetic (AEM) surveys using airborne mobile platforms enable rapid and efficient exploration of areas where groundwork is difficult. They have been widely used in fields such as shallow resource exploration and environmental engineering. Three-dimensional AEM inversion is the main technique used in fine structural interpretation. However, most current methods focus on separate component data inversions, which limit the kinds of structures that can be recovered in the inversion results. To address this issue, a method for the robust 3D joint inversion of multicomponent frequency-domain AEM data was developed in this study. First, a finite element method based on unstructured tetrahedral grids was used to solve the forward problem of frequency-domain AEM data for both isotropic and anisotropic media. During inversion, a limited-memory quasi-Newton (L-BFGS) method was used to reduce the memory requirements and enable the joint inversion of large-scale multicomponent AEM data. The effectiveness of our algorithm was demonstrated using synthetic models for both isotropic and anisotropic cases, with 5% Gaussian noise added to the modeling data to simulate the measured data for separate and joint inversions. The results of the synthetic models show that joint inversion has advantages over separate inversion in that it enables the recovery of finer underground structures and provides a novel approach for the fine interpretation of frequency-domain AEM data. Full article

The finite-element (FE) method for three-dimensional (3D) airborne electromagnetic (AEM) modeling can flexibly simulate complex geological structures at high accuracy. However, it has low efficiency and high computational requirements. To solve these problems, one needs to generate meshes more reasonably. In view of this, we develop an adaptive octree meshing scheme for frequency-domain AEM modeling. The octree meshes have the characteristics of regularity and flexibility, while the adaptive algorithm can effectively refine the mesh locally. In our adaptive mesh generation, the posterior errors and weighted coefficients are used to construct the final weighted posterior errors. We verify the accuracy of our method by comparing its results with semi-analytical solutions for a half-space model. Furthermore, we use the spectral-element (SE) method and our method to calculate EM responses for an abnormal block model and compare their computational costs. The results show that our adaptive scheme has obviously technical advantages over SE method for AEM modeling with multiple frequencies and multiple survey stations. Finally, we calculate a model with complex geological structures to verify the feasibility of our algorithm in complex geological circumstances. Full article

Full 3D inversion of time-domain Airborne ElectroMagnetic (AEM) data requires specialists’ expertise and a tremendous amount of computational resources, not readily available to everyone. Consequently, quasi-2D/3D inversion methods are prevailing, using a much faster but approximate (1D) forward model. We propose an appraisal tool that indicates zones in the inversion model that are not in agreement with the multidimensional data and therefore, should not be interpreted quantitatively. The image appraisal relies on multidimensional forward modeling to compute a so-called normalized gradient. Large values in that gradient indicate model parameters that do not fit the true multidimensionality of the observed data well and should not be interpreted quantitatively. An alternative approach is proposed to account for imperfect forward modeling, such that the appraisal tool is computationally inexpensive. The method is demonstrated on an AEM survey in a salinization context, revealing possible problematic zones in the estimated fresh–saltwater interface. Full article

From the first use of airborne electromagnetic (AEM) systems for remote sensing in the 1950s, AEM data acquisition, processing and inversion technology have rapidly developed. Once used extensively for mineral exploration in its early days, the technology is increasingly being applied in other industries alongside ground-based investigation techniques. This paper reviews the application of onshore AEM in Norway over the past decades. Norway’s rugged terrain and complex post-glacial sedimentary geology have contributed to the later adoption of AEM for widespread mapping compared to neighbouring Nordic countries. We illustrate AEM’s utility by using two detailed case studies, including time-domain and frequency domain AEM. In both cases, we combine AEM with other geophysical, geological and geotechnical drillings to enhance interpretation, including machine learning methods. The end results included bedrock surfaces predicted with an accuracy of 25% of depth, identification of hazardous quick clay deposits, and sedimentary basin mapping. These case studies illustrate that although today’s AEM systems do not have the resolution required for late-phase, detailed engineering design, AEM is a valuable tool for early-phase site investigations. Intrusive, ground-based methods are slower and more expensive, but when they are used to complement the weaknesses of AEM data, site investigations can become more efficient. With new developments of drone-borne (UAV) systems and increasing investment in AEM surveys, we see the potential for continued global adoption of this technology. Full article

Discrete phreatophytic vegetation associated with organic mound springs is present in several places in the semi-arid Walyarta Conservation Park (Park) in northern Western Australia. The mound springs are heritage listed, having significant cultural and environmental significance. Increased industrial (mining and agriculture) development in the region, coupled with a growing demand for groundwater to support these developments, requires an enhanced understanding of how the springs operate and the source of water that sustains their presence. The springs are broadly believed to be situated on geological faults and receive groundwater from artesian sources. However, their association with deeper geological structures and aquifer systems, the focus of this study, is not well understood. This study employed regional- and finer-scale airborne geophysical data, including electromagnetics (AEM) and magnetics, to constrain the sub-basin-scale hydrogeology of the West Canning Basin in Western Australia and to detail tectonic deformation, sedimentological and hydrological processes. The AEM data were inverted using 1- and 2D methods to better define structural discontinuities in the Park, and the results identified the location of faults and other geological structures that were coincident with spring locations. A complementary analysis of spatiotemporal patterns of green vegetation was undertaken using remote sensing data. A model for the extent of green vegetation (in percent), calculated using a constrained linear spectral unmixing algorithm and applied to a select Landsat Thematic Mapper ™ image archive, showed the persistence of green vegetation aligned with interpreted fault systems through extended dry periods. These geophysical and remotely sensed datasets demonstrate that in the Park, the sedimentary aquifers and landscapes are highly compartmentalized and that this constrains aquifer distribution, groundwater quality and the location of wetlands and phreatophytic vegetation. Integrating key information from these datasets allows for the construction of a three-dimensional model that predicts the nature and extent of the critical zone which sustains perennial groundwater discharge within mound springs, drainages and wetlands and provides a framework to assess discharge rates, mixing and, ultimately, sensitivity to changed water availability. Full article

Rocks and ores in nature usually appear macro-anisotropic, especially in sedimentary areas with strong layering. This anisotropy will lead to false interpretation of electromagnetic (EM) data when inverted under the assumption of an isotropic earth. However, the time-domain (TD) airborne EM (AEM) inversion for an anisotropic model has not attracted much attention. To get reasonable inversion results from TD AEM data, we present in this paper the forward modeling and inversion methods based on a triaxial anisotropic model. We apply three-dimensional (3D) finite-difference on the secondary scattered electric field equation to calculate the frequency-domain (FD) EM responses, then we use the inverse Fourier transform and waveform convolution to obtain TD responses. For the regularized inversion, we calculate directly the sensitivities with respect to three diagonal conductivities and then use the Gauss–Newton (GN) optimization scheme to recover model parameters. To speed up the computation and to reduce the memory requirement, we adopt the moving footprint concept and separate the whole model into a series of small sub-models for the inversion. Finally, we compare our anisotropic inversion scheme with the isotropic one using both synthetic and field data. Numerical experiments show that the anisotropic inversion has inherent advantages over the isotropic ones, we can get more reasonable results for the anisotropic earth structures. Full article

Airborne electromagnetic (AEM) method uses aircraft as a carrier to tow EM instruments for geophysical survey. Because of its huge amount of data, the traditional AEM data inversions take one-dimensional (1D) models. However, the underground earth is very complicated, the inversions based on 1D models can frequently deliver wrong results, so that the modeling and inversion for three-dimensional (3D) models are more practical. In order to obtain precise underground electrical structures, it is important to have a highly effective and efficient 3D forward modeling algorithm as it is the basis for EM inversions. In this paper, we use time-domain spectral element (SETD) method based on Gauss-Lobatto-Chebyshev (GLC) polynomials to develop a 3D forward algorithm for modeling the time-domain AEM responses. The spectral element method combines the flexibility of finite-element method in model discretization and the high accuracy of spectral method. Starting from the Maxwell's equations in time-domain, we derive the vector Helmholtz equation for the secondary electric field. We use the high-order GLC vector interpolation functions to perform spectral expansion of the EM field and use the Galerkin weighted residual method and the backward Euler scheme to do the space- and time-discretization to the controlling equations. After integrating the equations for all elements into a large linear equations system, we solve it by the multifrontal massively parallel solver (MUMPS) direct solver and calculate the magnetic field responses by the Faraday's law. By comparing with 1D semi-analytical solutions for a layered earth model, we validate our SETD method and analyze the influence of the mesh size and the order of interpolation functions on the accuracy of our 3D forward modeling. The numerical experiments for typical models show that applying SETD method to 3D time-domain AEM forward modeling we can achieve high accuracy by either refining the mesh or increasing the order of interpolation functions. Full article

Impermeable aquifer boundaries affect the flow of groundwater, transport of contaminants, and the drawdown of water levels in response to pumping. Hydraulic methods can detect the presence of such boundaries, but these methods are not suited for mapping complex, 3D geological bodies. Airborne electromagnetic (AEM) methods produce 3D geophysical images of the subsurface at depths relevant to most groundwater investigations. Interpreting a geophysical model requires supporting information, and hydraulic heads offer the most direct means of assessing the hydrostratigraphic function of interpreted geological units. This paper presents three examples of combined hydraulic and AEM analysis of impermeable boundaries in glacial deposits of eastern Nebraska, USA. Impermeable boundaries were detected in a long-term hydrograph from an observation well, a short-duration pumping test, and a water table map. AEM methods, including frequency-domain and time-domain AEM, successfully imaged the impermeable boundaries, providing additional details about the lateral extent of the geological bodies. Hydraulic head analysis can be used to verify the hydrostratigraphic interpretation of AEM, aid in the correlation of boundaries through areas of noisy AEM data, and inform the design of AEM surveys at local to regional scales. Full article

A lack of closely spaced datasets on layer elevations, aquifer parameters, identification of areas with high recharge potential, dominant conduit porosity zones, and well defined boundary conditions hampers the ability of groundwater models to produce a reliable water balance. Typically, geological structure, aquifer properties, and groundwater heads are obtained from point measurements which are sparse. The drillhole information in aquifers is usually available at locations far apart, distances ranging from hundreds to thousands of meters. Furthermore, pump tests are usually conducted at limited locations and generalized to the aquifer. This limited knowledge leads to errors in the conceptual understanding of the aquifer. In this study, Airborne Electromagnetic Survey (AEM) was used to define base elevations of the aquifers where drillhole information was lacking. Surface Nuclear Magnetic Resonance (sNMR), borehole NMR, Transient Electromagnetic (TEM), and downhole geophysical surveys have given new insight to the conceptualization of hydrogeological framework. These methods are relatively low in cost compared to traditional well drilling and provide information on layer elevations, aquifer parameters, point and diffuse recharge zones, and conduit porosity zones in the profile, which improves our definition of the boundary conditions. From a practical point of view, combining drillhole information with a variety of geophysical techniques provides sound datasets to develop a comprehensive conceptual model. This in turn can be used to build a robust groundwater model. Full article

The investigation depth of transient electromagnetic sensors can be effectively increased by reducing the system noise, which is mainly composed of sensor internal noise, electromagnetic interference (EMI), and environmental noise, etc. A high-sensitivity airborne transient electromagnetic (AEM) sensor with low sensor internal noise and good shielding effectiveness is of great importance for deep penetration. In this article, the design and optimization of such an AEM sensor is described in detail. To reduce sensor internal noise, a noise model with both a damping resistor and a preamplifier is established and analyzed. The results indicate that a sensor with a large diameter, low resonant frequency, and low sampling rate will have lower sensor internal noise. To improve the electromagnetic compatibility of the sensor, an electromagnetic shielding model for a central-tapped coil is established and discussed in detail. Previous studies have shown that unclosed shields with multiple layers and center grounding can effectively suppress EMI and eddy currents. According to these studies, an improved differential AEM sensor is constructed with a diameter, resultant effective area, resonant frequency, and normalized equivalent input noise of 1.1 m, 114 m², 35.6 kHz, and 13.3 nV/m², respectively. The accuracy of the noise model and the shielding effectiveness of the sensor have been verified experimentally. The results show a good agreement between calculated and measured results for the sensor internal noise. Additionally, over 20 dB shielding effectiveness is achieved in a complex electromagnetic environment. All of these results show a great improvement in sensor internal noise and shielding effectiveness. Full article

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014. Full article