Airborne Electromagnetic Induction Research Articles

Flexible quasi-2D inversion of time-domain AEM data, using a wavelet-based complexity measure

SUMMARYRegularization methods improve the stability of ill-posed inverse problems by introducing some a priori characteristics for the solution such as smoothness or sharpness. In this contribution, we propose a multidimensional scale-dependent wavelet-based ℓ1-regularization term to cure the ill-posedness of the airborne (time-domain) electromagnetic induction inverse problem. The regularization term is flexible, as it can recover blocky, smooth and tunable in-between inversion models, based on a suitable wavelet basis function. For each orientation, a different wavelet basis function can be used, introducing an additional relative regularization parameter. We propose a calibration method to determine (an educated initial guess for) this relative regularization parameter, which reduces the need to optimize for this parameter and, consequently, the overall computation time is under control. We apply our novel scheme to a time-domain airborne electromagnetic data set in Belgian saltwater intrusion context, but the scheme could equally apply to any other 2D or 3D geophysical inverse problem.

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Imaging subsurface resistivity structure from airborne electromagnetic induction data using deep neural network

ABSTRACTDue to the rapid development and spread of deep learning technologies, potential applications of artificial intelligence technology in the field of geophysical inversion are being explored. In this study, we applied a deep neural network (DNN) to reconstruct one-dimensional electrical resistivity structures from airborne electromagnetic (AEM) data for varying sensor heights. We used numerical models and their simulated AEM responses to train the DNN to be an inversion operator, and determined that it was possible to train the DNN without the use of stabilisers on the subsurface structures. We compared the quantitative performance of DNN and Gauss–Newton inversion of synthetic datasets, and demonstrated that DNN inversion reconstructed the subsurface structure more accurately, and within a significantly shorter period. We subsequently applied DNN inversion to a field dataset to quantify the effectiveness and applicability of the proposed method for real data. The results of the current study will open new avenues for real-time imaging of subsurface structures from AEM data.

A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data

Abstract. Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. Relative uncertainties associated with this method are large over thin ice regimes. Another retrieval method is based on the evaluation of surface brightness temperature (TB) in L-band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer-based method looses sensitivity for thick sea ice (> 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0.7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes. However, in mixed first-year (FYI) and multiyear (MYI) ice regimes as in the Beaufort Sea, the CS2 retrieval shows the lowest bias.

Identifying Ground-water Resources and Intrabasinal Faults in the Hueco Bolson, West Texas, using Airborne Electromagnetic Induction and Magnetic-field Data

We conducted a high-resolution airborne geophysical survey at the eastern margin of the basin-and-range province of the southwestern U.S. to examine the hydrostratigraphy of the Hueco Bolson and identify possible ground-water resources in this arid area. The survey, flown in 2001 over a 372-km2 area east of El Paso, Texas, acquired time-domain electromagnetic induction (TDEM) and magnetic field data. These data were used to map conductivity trends to depths of at least 200 m that are related to lateral and vertical changes in lithology, water content, water chemistry (EM data), basin geometry, and the location of intrabasinal faults (magnetic data). Pre-survey, ground-based TDEM soundings established the achievable exploration depths and demonstrated that the relatively deep groundwater (80 to 120 m) significantly influenced the transient signal and was within the exploration depth of the airborne system. Airborne EM and magnetic field data identified intrabasinal faults that influence basin-fill deposition. Conductivity-depth slices constructed from airborne TDEM data allowed lateral variations in water quality and lithology to be mapped that helped predict ground-water resource quality within these fault-bounded basins filled with alluvial, lacustrine, and eolian sediments.

Characterizing initial-state conductivity distribution at a CO2 injection site with airborne, surface, and borehole electromagnetic induction methods

Electromagnetic (EM) methods were used to characterize (1) the general near-surface geology and stratigraphy and (2) the initial electrical conductivity distribution at a enhanced oil recovery (EOR) site to assess and monitor possible near-surface environmental impacts of a carbon sequestration experiment. The field study was conducted at Cranfield Field, an EOR site where is being injected into a depleted oil and gas reservoir in the Cretaceous lower Tuscaloosa Formation in western Mississippi. The study focused on Tertiary and younger strata between the ground surface and maximum depths of approximately 200 m (656 ft) that host groundwater more than 3000 m (9843 ft) above the oil and gas reservoir and injection zone. It included an airborne geophysical survey collecting frequency-domain EM data, time-domain surface EM measurements, borehole logging with EM induction, natural gamma spectra, and water-level measurements. Different approaches of temperature drift corrections for the borehole EM data were compared; good results of consistent and accurate conductivity values were produced by combining both directions of a two-way (uphole and downhole) measurement. The airborne EM provided data over a large area with sufficient detail to give an overview for the subsequent surface and borehole surveys, the surface time-domain data gave insight into greater depths, and the borehole induction data provided the necessary details. These three EM methods complement each other in areal coverage, lateral and vertical resolution, and exploration depth. Together, they can provide a comprehensive near-surface characterization of the study area that is necessary to establish initial-state conditions that support future monitoring of potential migration to the near-surface environment.

Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard

AbstractWe present sea-ice surface roughness estimates, i.e. the standard deviation of relative surface elevation, in the Arctic regions of Fram Strait and the Nansen Basin north of Svalbard acquired by an airborne laser scanner and a single-beam laser altimeter in 2010. We compare the scanner to the altimeter and compare the differences between the two survey regions. We estimate and correct sensor roll from the scanner data using the hyperbolic response of the scanner over a flat surface. Measurement surveys had to be longer than 5 km north of Svalbard and longer than 15 km in Fram Strait before the statistical distribution in surface roughness from the scanner and altimeter became similar. The shape of the surface roughness probability distributions agrees with those of airborne electromagnetic induction measurements of ice thickness. The ice in Fram Strait had a greater mean surface roughness, 0.16 m vs 0.09 m, and a wider distribution in roughness values than the ice in the Nansen Basin. An increase in surface roughness with increasing ice thickness was observed over fast ice found in Fram Strait near the coast of Greenland but not for the drift ice.

Sensitivity of simulated Arctic sea ice to realistic ice thickness distributions and snow parameterizations

Sea ice and snow on sea ice to a large extent determine the surface heat budget in the Arctic Ocean. In spite of the advances in modeling sea‐ice thermodynamics, a good number of models still rely on simple parameterizations of the thermodynamics of ice and snow. Based on simulations with an Arctic sea‐ice model coupled to an ocean general circulation model, we analyzed the impact of changing two sea‐ice parameterizations: (1) the prescribed ice thickness distribution (ITD) for surface heat budget calculations, and (2) the description of the snow layer. For the former, we prescribed a realistic ITD derived from airborne electromagnetic induction sounding measurements. For the latter, two different types of parameterizations were tested: (1) snow thickness independent of the sea‐ice thickness below, and (2) a distribution proportional to the prescribed ITD. Our results show that changing the ITD from seven uniform categories to fifteen nonuniform categories derived from field measurements, and distributing the snow layer according to the ITD, leads to an increase in average Arctic‐wide ice thickness by 0.56 m and an increase by 1 m in the Canadian Arctic Archipelago and Canadian Basin. This increase is found to be a direct consequence of 524 km3 extra thermodynamic growth during the months of ice formation (January, February, and March). Our results emphasize that these parameterizations are a key factor in sea‐ice modeling to improve the representation of the sea‐ice energy balance.

Large-scale ice thickness distribution of first-year sea ice in spring and summer north of Svalbard

AbstractThe large-scale thickness distribution of sea ice was measured during several campaigns in the European Arctic north of Svalbard from 2007 using an airborne electromagnetic induction device. In August 2010 and April-May 2011, this was complemented by extensive on-ice work including measurements of snow thickness and freeboard. Ice thicknesses show a clear difference between the seasons, with thicker ice during spring than in summer. In spring 2011, negative freeboard and flooding were observed as a result of the extensive snow cover. We find that the characteristics of the first-year sea ice allow combining observations from different years. The ice thickness in the marginal ice zone increases with increasing latitude and increasing distance to the ice edge; however, in the inner ice pack from ∼100 km from the ice edge the thickness remains almost constant. Modal ice thickness in spring reaches 2.4 m whereas in summer it is 1.0–1.4 m. Our study provides new insight into ice thickness distributions of a typical ice cover consisting of mainly first- and second-year ice, which may become the dominant ice type in the Arctic in the future.

A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with a strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a novel automated method for ice concentration and thickness analysis combining modeling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modeling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness. The algorithms can be run automatically, and the final products can then be used by ice analysts for operational ice service. The method is globally applicable to all seas where SAR data are available.

Characterizing oil field salinization using airborne, surface, and borehole geophysics: An example from the Upper Colorado River Basin, Texas

Multidisciplinary investigations of natural and oil field salinization along the upper Colorado River, Texas, present an opportunity to integrate results from a stream-axis airborne geophysical survey, ground and borehole geophysical surveys, and well drilling and sampling. Airborne electromagnetic (EM) induction measurements along 437 km (272 mi) of river and tributary stream axes identified discrete salinized streambed segments, including several near oil fields. Identification of these salinized streambed segments allowed more intensive and invasive investigations to be focused on the most significant near-river sources of salinity. One of these streambed segments lies adjacent to an oil field, where production began in the 1950s before discharge of coproduced brine into surface pits was prohibited in Texas. Monitor wells drilled after the airborne survey verified groundwater salinization in the oil field but did not adequately delineate salinization nor identify specific salinity source areas. Subsequent ground and borehole geophysical surveys complemented airborne EM induction and well data by establishing lateral and vertical salinization bounds in the oil field, discovering possible salinity source areas, and determining optimal locations for additional wells.

Introduction to the JEEG Agricultural Geophysics Special Issue

Near-surface geophysical methods have become increasingly important tools in applied agricultural practices and studies. The great advantage of geophysical methods is their potential rapidity, low cost, and spatial continuity when compared to more traditional methods of assessing agricultural land, such as sample collection and laboratory analysis. Agricultural geophysics investigations commonly focus on obtaining information within the soil profile, which generally does not extend much beyond 2 meters beneath the ground surface. Although the depth of interest oftentimes is rather shallow, the area covered by an agricultural geophysics survey can vary widely in scale, from experimental plots (10 s to 100 s of square meters), to farm fields (10 s to 100 s of hectares), up to the size of watersheds (10 s to 100 s of square kilometers). To date, three predominant methods—resistivity, electromagnetic induction (EMI), and ground-penetrating radar (GPR)—have been used to obtain surface-based geophysical measurements within agricultural settings. However, a recent conference on agricultural geophysics (Bouyoucos Conference on Agricultural Geophysics, September 8–10, 2009, Albuquerque, New Mexico; www.ag-geophysics.org) illustrated that other geophysical methods are being applied or developed. These include airborne electromagnetic induction, magnetometry, seismic, and self-potential methods. Agricultural geophysical studies …

Combining airborne electromagnetic induction and hydrochemistry to quantify salinity contributions to a large basin stream, Colorado River, Texas, USA

ABSTRACTWe combined multifrequency airborne electromagnetic induction (EM) measurements of apparent ground conductivity with chemical analyses of surface water to delineate natural and oilfield salinity sources that degrade surface water quality by elevating total dissolved solids, chloride and sulphate concentrations along several hundred kilometres of the Colorado River (western Texas, USA). To reduce the cost of airborne geophysical surveying over such large areas, we used a helicopter to tow an EM instrument at low altitude along the stream‐axis and measure the apparent electrical conductivity of the ground at multiple frequencies, examined results in the field to identify salinized stream segments and optimal water sampling locations and then flew more detailed surveys over these limited areas rather than over the entire basin as is typical in salinization studies. Minimally processed stream‐axis EM data (including apparent conductivities measured at single frequencies and multifrequency ‘spectrograms’ along the stream‐axis) helped identify salinized streambed segments, discriminate between surface and subsurface sources of salinity and determine water sampling locations upstream and downstream from each segment. We integrated EM, streamflow and hydrochemical data to calculate salinity loads, identify specific natural and oilfield salinity sources and guide and implement remediation efforts.Stream‐axis flight lines offer the advantage of rapidly acquiring high‐resolution subsurface conductivity data along long stream segments where traditional gridded flight‐line surveys and waterborne measurements are impractical or prohibitively expensive. They also overcome difficulties associated with topographic effects when surveying deeply incised streams. Such surveys provide valuable information on location, extent and type of salinization and can guide water sampling and more intensive ground or airborne measurements.

Application of time-domain airborne electromagnetic induction to hydrogeologic investigations on the Pajarito Plateau, New Mexico, USA

We conducted a time-domain airborne electromagnetic (AEM) survey of part of the semiarid Pajarito Plateau of northern New Mexico to determine depths and lateral extent of perched aquifers in the vadose zone and depths and pathways of infiltration to the regional aquifer. The electrical resistivity of the plateau ranged over three orders of magnitude ([Formula: see text] to [Formula: see text]) to a depth of at least [Formula: see text]. Borehole and surface-derived data allow the correlation of resistivity images with the hydrogeology of the plateau. As expected, water exerts a significant control on resistivity. However, the presence of large amounts (up to 90%) of clay in some units, in conjunction with water, also has a significant effect, lowering resistivity (to [Formula: see text]) more than the presence of clay-free saturated zones alone. Because of the resulting low resistivity, we are able to better delineate a large,known volume of clay-altered volcaniclastic rock and postulate the presence of another. Resistivity values of [Formula: see text] cor-relate with depths to saturated zones where no clay is present, but they do not allow us to distinguish between one large or several smaller perched groundwater zones and the underlying regional zone of saturation. We imaged a region of significant infiltration related to a sewage treatment plant and to near-surface hydrogeo-logic conditions conducive to infiltration and correlated with a region of preferential transport of anthropogenic chemicals through the vadose zone. AEM data provide an important synop-tic view of the shallow (few hundred meters) resistivity structure of the plateau. Although interpretation of the data is not unique, when combined with borehole geologic, hydrologic, and geo-chemical data, it can provide relative depths to saturated zones, delineate regions of high clay content (zones of alteration), and image regions of recharge to the regional aquifer.

Determining salinization extent, identifying salinity sources, and estimating chloride mass using surface, borehole, and airborne electromagnetic induction methods

Using an example from an oil field in the semiarid Red River basin in Texas, we show that electromagnetic (EM) methods are useful in locating salinized soil and water, determining salinization extent, identifying likely salinity sources, and estimating the total mass of chloride within a saline‐water plume. Each of these aspects assists in managing salinization and assessing its impact. We used ground EM instruments to establish salinization boundaries and determine the range of electrical conductivity, airborne measurements to locate potential sources and quantify the lateral extent and intensity of salinization, and borehole measurements and time domain EM soundings to determine salinization depth and relate ground conductivity to chloride content. We estimated infiltration volume and total chloride mass in the plume from EM data and an empirical, site‐specific chloride:conductivity ratio established from well data. Because the measured conductivity of water strongly correlates with total dissolved solids concentration, mass estimation could be extended to any ionic constituent that covaries linearly with total dissolved solids concentration. EM methods owe their success to the large increase in electrical conductivity that occurs where highly conductive, saline water infiltrates geologic materials having naturally low conductivities.

Airborne resistivity mapping

A brief overview is presented of the theory and techniques for mapping the electrical resistivity of surficial formations by interpreting airborne electromagnetic induction data. Although the method has not yet reached its full potential, useful information can be obtained on the thickness, the conductivity, and the depth below the surface of any conductive unconsolidated material. Practical applications of this geophysical method include the mapping of aquifers, the delineation of salt-water intrusions, and the estimation of the depth of valley fill material. The instrumentation is described and both time- and frequency-domain systems are discussed. Methods and examples of automated data interpretation are given. On the basis of the results available it is concluded that with the available equipment and using the limited methods of interpretation described, one can map the thickness of the uppermost layer (be it conductive or resistive) to a depth somewhat greater than 50 m with an accuracy of about 10-20 m.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>