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High-bandwidth magnetic data are normally distorted by ubiquitous 50- or 60-Hz noise from powerlines and similar sources. The powerline noise can be orders of magnitude larger than the magnetic signal from targets and must be culled from data sets prior to interpretation. Suppression of the powerline noise by simple filtering can result in artifacts and an unacceptable reduction in resolution of ground-based and unmanned aerial vehicle magnetic surveys. Removal approaches such as those based on Biot-Savart modeling are sensitive to the estimated position of the powerline systems, in addition to their limited applicability due to the requirement of a DC source. Moreover, the powerline noise in data acquired from a moving platform is inherently nonstationary and removal techniques must be specifically developed with this in mind. We propose a model-based method that does not rely on a priori knowledge of the powerline system by fitting and subtracting a set of sinusoids to the data. These sinusoids are computed on small windows of data, tied together with regularization terms within the fitting process to reduce discontinuities between segments. We further incorporate powerline frequency as a nonlinear parameter, allowing for fluctuations in the fundamental frequency as loads on the power grid change. Through synthetic and field examples, we show that periodic noise can be reliably removed automatically without the need for filtering or significant alterations of the frequency content. Powerline noise is reduced by over 98% in the field example.

Imagining geological layers beneath lakes, rivers, and shallow seawater provides detailed information critical for hydrological modelling, geologic studies, contaminant mapping, and more. However, significant engineering and interpretation challenges have limited the applications, preventing widespread adoption in aquatic environments. We have developed a towed transient electromagnetic (tTEM) system to a new, easily configurable floating, transient electromagnetic instrument (FloaTEM) capable of imaging the subsurface beneath both fresh and saltwater water bodies. Based on the terrestrial tTEM instrument, the FloaTEM system utilizes a similar philosophy of a lightweight towed transmitter with a trailing, offset receiver, pulled by a small boat. The FloaTEM system is tailored to the specific fresh or saltwater application as necessary, allowing investigations down to 100 m in freshwater environments, and up to 20 m on saline waters. Through synthetic analysis we show how the depth of investigation of the FloaTEM system greatly depends on the resistivity and thickness of the water column. The system has been successfully deployed in Denmark for a variety of hydrologic investigations, improving the ability to understand and model processes beneath water bodies. We present two freshwater applications and a saltwater application. Imaging results reveal significant heterogeneities in the sediment types below the freshwater lakes. The saline water example demonstrates that the system is capable to identify and distinguish clay and sand layers below the saline water column.

Aufeis are sheets of ice unique to cold regions that originate from repeated flooding and freezing events during the winter. They have hydrological importance associated with summer flows and winter insulation, but little is known about the seasonal dynamics of the unfrozen sediment layer beneath them. This layer may support perennial groundwater flow in regions with otherwise continuous permafrost. For this study, ground penetrating radar (GPR) were collected in September 2016 (maximum thaw) and April 2017 (maximum frozen) at the Kuparuk aufeis field on the North Slope of Alaska. Supporting surface nuclear magnetic resonance data were collected during the maximum frozen campaign. These point-in-time geophysical data sets were augmented by continuous subsurface temperature data and periodic Structure-from-Motion digital elevation models collected seasonally. GPR and difference digital elevation model data showed up to 6 m of ice over the sediment surface. Below the ice, GPR and nuclear magnetic resonance identified regions of permafrost and regions of seasonally frozen sediment (i.e., the active layer) underlain by a substantial lateral talik that reached >13-m thickness. The seasonally frozen cobble layer above the talik was typically 3- to 5-m thick, with freezing apparently enabled by relatively high thermal diffusivity of the overlying ice and rock cobbles. The large talik suggests that year-round groundwater flow and coupled heat transport occurs beneath much of the feature. Highly permeable alluvial material and discrete zones of apparent groundwater upwelling indicated by geophysical and ground temperature data allows direct connection between the aufeis and the talik below.

Time gating is a commonly used approach in the pre-processing of nuclear magnetic resonance (NMR) data before Laplace inversion. Gating suppresses spurious signals that can degrade recovered decay time distributions and therefore often stabilizes inversion. However, care must be taken in applying this technique to real world data where both non-Gaussian and correlated noise decrease the efficacy of noise reduction through stacking. If not properly accounted for, unreliable noise estimates introduce inversion artefacts. Fortunately, noise realization proxies obtained through data phasing can be used to bootstrap reliable confidence intervals for the windowed data. Benefits of the approach are demonstrated through inversion of synthetics as well as borehole data from a deep carbon capture and sequestration application. We also introduce an open source cross platform data processing utility with these capabilities which interested persons can use to explore the impacts of various processing workflows.

Fire can be a significant driver of permafrost change in boreal landscapes, altering the availability of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to fire-induced change. As fire frequency is expected to increase in the high latitudes, methods to understand the vulnerability and resilience of different landscapes to permafrost degradation are needed. We present a combination of multiscale remote sensing, geophysical, and field observations that reveal details of both near-surface ( 1 m) impacts of fire on permafrost. Along 11 transects that span burned-unburned boundaries in different landscape settings within interior Alaska, subsurface electrical resistivity and nuclear magnetic resonance data indicate locations where permafrost appears to be resilient to disturbance from fire, areas where warm permafrost conditions exist that may be most vulnerable to future change, and also areas where permafrost has thawed. High-resolution geophysical data corroborate remote sensing interpretations of near-surface permafrost and also add new high-fidelity details of spatial heterogeneity that extend from the shallow subsurface to depths of about 10 m. Results show that postfire impacts on permafrost can be variable and depend on multiple factors such as fire severity, soil texture, soil moisture, and time since fire.

Characterization of permafrost, particularly warm and near-surface permafrost which can contain significant liquid water, is critical to understanding complex interrelationships with climate change, ecosystems, and disturbances such as wildfires. Understanding the vulnerability and resilience of permafrost requires an interdisciplinary approach, relying on (for example) geophysical investigations, ecological characterization, direct observations, remote sensing, and more. As part of a multi-year investigation into the impacts of wildfires to permafrost, we have collected in situ measurements of the nuclear magnetic resonance (NMR) response of active layer and permafrost in a variety of soil conditions, types, and saturations. In this paper, we summarize the NMR data and present quantitative relationships between active layer and permafrost liquid water content and pore sizes. Through statistical analyses and synthetic freezing simulations, we also demonstrate that borehole NMR can image the nucleation of ice within soil pore spaces.

Systematic calibration errors must be taken into account because they can substantially impact the accuracy of inverted subsurface resistivity models derived from frequency-domain electromagnetic data, resulting in potentially misleading interpretations. We have developed an approach that uses data acquired at multiple elevations over the same location to assess calibration errors. A significant advantage is that this method does not require prior knowledge of subsurface properties from borehole or ground geophysical data (though these can be readily incorporated if available), and is, therefore, well suited to remote areas. The multielevation data were used to solve for calibration parameters and a single subsurface resistivity model that are self consistent over all elevations. The deterministic and Bayesian formulations of the multielevation approach illustrate parameter sensitivity and uncertainty using synthetic- and field-data examples. Multiplicative calibration errors (gain and phase) were found to be better resolved at high frequencies and when data were acquired over a relatively conductive area, whereas additive errors (bias) were reasonably resolved over conductive and resistive areas at all frequencies. The Bayesian approach outperformed the deterministic approach when estimating calibration parameters using multielevation data at a single location; however, joint analysis of multielevation data at multiple locations using the deterministic algorithm yielded the most accurate estimates of calibration parameters. Inversion results using calibration-corrected data revealed marked improvement in misfit, lending added confidence to the interpretation of these models.

Line spacing and flight height are critical parameters in airborne gravity gradient surveys; the optimal trade-off between survey costs and desired resolution, however, is different for every situation. This article investigates the additional benefit of reducing the flight height and line spacing though a study of a survey conducted over the Great Sand Dunes National Park and Preserve, which is the highest-resolution public-domain airborne gravity gradient data set available, with overlapping high- and lower-resolution surveys. By using Fourier analysis and matched filtering, it is shown that while the lower-resolution survey delineates the target body, reducing the flight height from 80 m to 40 m and the line spacing from 100 m to 50 m improves the recoverable resolution even at basement depths.

Statistical de-noising and compressive inversion methods based on Principal Component Analysis can reduce random noise, separate desired signals from correlated noise, and improve the efficiency and results of airborne EM inversions. However, inversion of PCA-processed data with standard kernels produces inaccurate results due to the improper forward mapping operators used. These inversions must incorporate the PCA rotation in the inversion process for accurate results. In order to appropriately apply these operators to the inversion kernels, the statistical distribution of the noise before and after processing and its effect on the data misfit must be understood. We can then develop compressive inversion techniques utilising PCA. In this presentation, we demonstrate the need for incorporation of rotation into the inversion kernels through linear examples and show the utility of principal component analysis in compressive inversion. We then examine the statistical distribution of TEM data and noise and show that the noise follows a multivariate t-distribution both before and after processing with PCA. We conclude by introducing a compressive inversion technique formulated in the principal component domain.

We present a method for modelling the terrain response of gravity gradiometry surveys utilising an adaptive quadtree mesh discretization. The data- and terrain-dependent method is tailored to provide rapid and accurate terrain correctionsfordrapedandbarometricairbornesurveys.Thesurfaceusedinthemodellingoftheterraineffectforeachdatum is discretized automatically to the largest cell size that will yield the desired accuracy, resulting in much faster modelling than full-resolution calculations. The largest cell sizes within the model occur in areas of minimal terrain variation and atlargedistancesawayfromthedatumlocation.Weshowsyntheticand fieldexamplesforproofofconcept.Inthepresented field example, the adaptive quadtree method reduces the computational cost by performing 351 times fewer calculations than the full model would require while retaining an accuracy of one Eotvos for the gradient data. The method is also used for the terrain correction of the gravity field and performed 310 times faster compared with a calculation of the full digital elevation model.

Proper terrain correction in onshore airborne gravity gradiometry surveys requires detailed digital elevation models(DEMs).However,thehorizontalresolutionrequirementofDEMsisnotwellexploredinliterature.Asaresult,there is no standardized horizontal resolution used in commercial surveys. Also, the effect of requisite low-pass filtering applied during and subsequent to acquisition has not been discussed within this context. We address these issues by quantifying the optimalhorizontalresolutionusinganexampledatasetandapracticalmethodforperformingterraincorrectionsinbarometric surveys, based on Parker's 1972 Fourier-domain calculation. We also quantify the required spatial extents of DEMs (including grid padding) by deriving empirical rules based on numerical simulations. With these parameters quantified, we provide the basis for optimising the terrain correction to improve efficiency in not only gravity gradiometry terrain corrections and forward modelling, but also in DEM acquisition survey design.

SUMMARY Proper terrain correction in airborne and seaborne gravity gradiometry surveys requires detailed digital elevation models (DEMs). However, due to standard low-pass filter techniques applied during acquisition, the required resolution of the DEMs is altered in commercial surveys. We quantify the ideal resolution for an example dataset with a new and practical method for performing terrain corrections based on Parker's 1972 Fourier domain calculation. In the same vein, we quantify the required spatial extents of DEMs for nominal cases. With these parameters quantified, we can optimize the terrain correction to improve efficiency in both gravity gradiometry forward modeling as well as terrain corrections. In an ideal case, we find that the required spatial extent of the DEM is roughly nine times the greatest relief in the terrain outside the survey area. For the specific case, we find that 20 meter resolution is suitable.

In porous materials, susceptibility contrasts between the matrix and the pore fluid generate pore-scale inhomogeneities in the magnetic field that are referred to as internal gradients. Internal gradients impact NMR measurements, and can cause large errors in the calculated diffusion coefficient if they are not accounted for. The magnitude of the internal gradients is determined by the susceptibility contrast, the strength of the background magnetic field, and the pore geometry. We use statistical analysis to look for correlation between measured internal gradients and properties of sediment samples. The primary goal of this analysis was to identify parameters that could be used as predictors of internal gradient magnitudes. We measured internal gradients using two different NMR methods: Method 1 estimates an average effective gradient, and Method 2 calculates a distribution of effective gradients. The sediment properties that we consider are magnetic susceptibility, iron content, specific surface area, grain size, and measured NMR parameters including the mean log T2 and the T1/T2 ratio. In our preliminary analysis, conducted with data from 20 sediment samples, we observe linear trends between iron content and measured gradients, and between magnetic susceptibility and measured gradients. We also see that the mineral form of iron appears to impact the relationships between iron content, magnetic susceptibility, and internal gradients. The correlation observed between gradients measured with Method 1 and both the specific surface area and T2 could indicate that this method is biased by relaxation time; this relationship was not observed for the gradients measured with Method 2. We plan to collect data on more sediment samples to better understand these relationships and develop a model for the estimation of internal gradients. Such a model will enable us to include internal gradient values in diffusion coefficient calculations for a range of nearsurface applications.

We present a study of a large airborne electromagnetic and magnetic survey acquired over the Alaska Highway Corridor. Several aspects of the conductivity structure are of interest in this area: very near surface conductivity to characterize active layer thickness, mid-depth conductivity for permafrost and geohazard applications, and deeper structures for regional mapping and potential mineral identification. Because of the wide variety of scales and targets, we present an integrated inversion approach, using disparate but complimentary techniques. In addition, we introduce a novel method of incorporation of magnetic susceptibility information into the conductivity inversions.

Inversion of airborne electromagnetic data is critical to resource exploration and characterization. Commonly, each sounding is inverted using a one-dimensional Earth approximation, mainly because two and three-dimensional inversions on that scale are computationally intractable at present. Fortunately, the one-dimensional Earth approximation is reasonable in most resource exploration settings, as the horizontal variation of the targets is roughly on the same scale or larger than the “footprint” of the transmitter. However, with the size of most airborne surveys, even the 1D approximation leads to large sets of inversions that cannot be computed within a reasonable time frame.

Statistical de-noising methods such as Principal Component Analysis modify data in a way not constrained by physics. In much the same way as frequency-filtered data must incorporate altered frequency content into numerical interpretation, so must statistically rotated data include the rotation operator in inversion processes. We propose a method of accounting for statistical reduction of data in non-linear inversions (such as for electromagnetic data) through an incorporation of the rotation operator into the kernels and sensitivity matrix. We show a generalized linear inversion to demonstrate the necessity of rotating the inversion kernels in this abstract and will present a nonlinear parametric example from an unexploded ordnance application.

SUMMARY Time-lapse gravity surveys directly detect mass changes and offer unique means for monitoring the dynamics of the subsurface. As wider application of the method is emerging, survey design and model appraisal are key steps in developing a meaningful interpretation. We first examine what the optimal station spacing should be, in theory and in inversion-based simulations given a scale-length feature. We then examine the resolving power if the data spacing has been established in order to have an appropriate model mesh.

We study the practical aspects of performing terrain corrections in airborne/seaborne gravity gradient surveys in terms of necessary spatial extents and required resolution of digital elevation models. We develop a new and practical method for performing terrain corrections through an extension of Parker’s 1972 method for calculating potential anomalies in the Fourier domain. We then study and present a discussion of the parameters necessary for an accurate and efficient terrain correction.