3D modelling of near-surface, environmental effects on AEM data

3D modelling of near-surface, environmental effects on AEM data

The acquisition of airborne electromagnetic (AEM) data colocated with water well data provides an essential data set for constructing an accurate rock-physics relationship between resistivity from AEM and lithology from wells. Results from an AEM survey in Butte and Glenn Counties, California, USA, where 41 water wells are located within 100 m of the AEM flight lines, provide a unique opportunity to investigate the effect of water wells on AEM data. Based on systematic numerical analyses and examination of the field data, we find that the only noise source related to the wells that affects the AEM signals in our study area is the power lines; the effect of the steel casings in the study area is negligible. Our numerical simulations yield two important findings: (1) the effect of the power line is mainly dependent upon the resistivity of the ground pathway between the base of the power-line poles, and (2) for the resistivity structure in the study area, when the ground contact resistance is sufficiently low, the effect of the power line will be observed in the AEM data until there is a separation distance of 174 m from the power line; the total power-line percentage effect less than 3% has been considered to be negligible. However, we find that, in the field data, an average separation distance between the power line and high-quality AEM data (i.e., no impact from the power lines) is only 60% of this predicted distance. We attribute this to the high ground contact resistance caused by degradation of the grounded electrodes located at the bottom of the power-line poles. For our 41 wells, after removing any soundings contaminated by power-line noise, we are left with high-quality soundings located 0–160 m from the water wells, with an average distance of 77 m.

Airborne electromagnetic (AEM) survey data was used to supplement geotechnical investigations for a highway construction project in Norway. Heterogeneous geology throughout the survey and consequent variable bedrock threshold resistivity hindered efforts to directly track depth to bedrock, motivating us to develop an automated algorithm to extract depth to bedrock by combining both boreholes and AEM data. We developed two variations of this algorithm: one using simple Gaussian or inverse distance weighting interpolators, and another using ordinary kriging and combined probability distribution functions of input parameters.Evaluation shows that for preliminary surveys, significant savings in boreholes required can be made without sacrificing bedrock model accuracy. In the case study presented, we estimate data collection savings of 1000 to 10,000NOK/km (c. $160 to $1600 USD/km) would have been possible for early phases of the investigation. However, issues with anthropogenic noise, low signal, and uncertainties in the inversion model likely reduced the comparative advantage that including AEM provided.AEM cannot supersede direct sampling where the model accuracy required exceed the resolution possible with the geophysical measurements. Nevertheless, with the algorithm we can identify high probability zones for shallow bedrock, identify steep or anomalous bedrock topography, and estimate the spatial variability of depth at earlier phases of investigation. Thus, we assert that our method is still useful where detailed mapping is the goal because it allows for more efficient planning of secondary phases of drilling.

Eureka! AEM uncovers more than expected Precompetitive AEM data and associated scientific analysis assists exploration under cover by reducing risk; stimulating investment and promoting exploration for commodities. In recent years, Geoscience Australia has flown three regional Airborne Electromagnetic (AEM) surveys covering three percent of Australia. Data and associated interpretations from regional surveys in the Paterson, Pine Creek and Lake Frome regions have led to tenement take up, stimulated exploration for a number of commodities and have given rise to many Eureka moments. This presentation highlights new results from the use of the regional AEM data and interpretations for commodities other than uranium; results that have been announced by industry via the Australian Stock exchange and other publications. A copper exploration company reported high-grade copper intercepts in target areas defined by data from the regional Paterson survey. A gold exploration company identified horizons associated with the gold-rich Cosmo-Howley corridor using the Pine Creek AEM data. The AEM data have also helped to identify Triassic coal measures at Leigh Creek.

Experimental airborne electromagnetic (AEM) survey data collected in Cape Cod Bay are used to derive continuous profiles of water depth, electrical depth, water conductivity, and bottom sediment conductivity. Through a few well-known empirical relationships, the conductivities are used, in turn, to derive density, porosity, sound speed, and acoustic reflectivity of the ocean bottom. A commercially available Dighem III AEM system was used for the survey without any significant modification. The helicopter-borne system operated at 385 and 7200 Hz; both were in a horizontal coplanar configuration. The interpreted profiles show good agreement with available ground truth data. Where no such data are available, the results appear to be very reasonable. Compared with the shipborne electrode array method, the AEM method can determine the necessary parameters at a much higher speed with a better lateral resolution over a wide range of water depths from 0 to perhaps 100 m. The bottom sediment conductivity that can be measured by the AEM method is closely related to physical properties of sediments, such as porosity, density, sound speed, and, indirectly, sediment types that might carry broad implications for various offshore activities.

The Frome airborne electromagnetic (AEM) survey was designed to provide reliable pre-competitive AEM data to aid the search for energy and mineral resources around the Lake Frome region of South Australia. Flown in 2010, a total of 32,317 line kilometres of high-quality airborne geophysical data were collected over an area of 95,450 km2 at a flight line spacing mostly of 2.5 km, opening to 5 km spaced lines in the Marree-Strzelecki Desert area to the north.Interpretations of the data show the utility of regional AEM surveying for mapping crucial elements of sandstone-hosted uranium mineral systems as well as for mapping geological surfaces, structures and depth of cover over a wide area. Data from the Frome AEM survey allow mineral explorers to put their own high-resolution AEM surveys into a regional context. Survey data were used to map and interpret a range of geological features that are associated with, or control the location of, sandstone-hosted uranium mineral systems, and have been used to assess the uranium prospectivity of new areas to the north of the Flinders Ranges.

Induced polarization (IP) effects are becoming more evident in time-domain helicopter airborne electromagnetic (AEM) data thanks to advances in instrumentation, mainly due to improvements in the signal-to-noise ratio and hence better data quality. Although the IP effects are often manifested as negative receiver voltage values, which are easy to detect, in some cases, IP effects can distort recovered transients in other ways so they may be less obvious and require careful data analysis and processing. These effects represent a challenge for modeling and inversion of the AEM data. For proper modeling of electromagnetic transients, the chargeability of the subsurface and other parameters describing the dispersion also need to be taken into consideration. We use the Cole-Cole model to characterize the dispersion and for modeling of the IP effects in field AEM data, collected by different airborne systems over different geologies and exploration targets, including examples from diamond, gold, and base metal exploration. We determined how multiparametric inversion techniques can simultaneously recover all four Cole-Cole parameters, including resistivity [Formula: see text], chargeability [Formula: see text], relaxation time [Formula: see text], and frequency parameter [Formula: see text]. The results obtained are in good agreement with the ancillary information available. Interpretation of the IP effects in AEM data is therefore seen by the authors as providing corrected electrical resistivity distributions, as well as additional information that could assist in mineral exploration.

ABSTRACTElectromagnetics (EM) has been used extensively for Volcanic Hosted Massive Sulphide (VHMS) exploration in Australia. Exploring under conductive cover introduces significant limitations when using EM to identify bedrock conductivity anomalies that may be associated with VHMS deposits. We present an alternative approach, whereby robust geological modelling of the Airborne Electromagnetic (AEM) data plays a major role in the exploration strategy in the Bryah and Yerrida Basins of central Western Australia. The AEM is not only used for the identification of bedrock conductors but also forms a critical dataset constraining a robust basin-wide geological model. This model is used to identify priority areas for follow up surface geophysics and geochemistry. A patchwork of AEM surveys, covering portions of the Bryah and Yerrida basins, has been acquired by various explorers and contractors during the last decade. Systems and system specifications vary greatly. Accordingly, accurate geological interpretation of a basin-scale area, flown using various systems, cannot be derived from either raw data or fast/approximate conductivity products provided by contractors. All datasets require reconciliation with a common workflow and robust modelling strategy. Historic AEM data acquired with different systems along the edges of the tenure have been reprocessed and inverted. The remaining central block awaits the contractor's arrival before the data is subject to the same workflow. The end result will be a seamless basin-wide 3D conductivity model (extending over 6500 km2), which will inform the geological interpretation and subsequent follow-up exploration efforts. The preliminary 3D models already allow clear identification and modelling of the pyritic shale horizons, enabling the anomalous geochemistry and strongly conductive nature of these units to be discounted in the targeting process

Airborne electromagnetic (AEM) data were collected offshore Busselton, Western Australia, using GEOTEMTM operating at both 12.5 Hz and 25 Hz over the same survey area. The survey covered a region in shallow water up to 35 m depth, and two overlapping tie lines skirting Cape Naturaliste extending into deeper water. The objectives were to compare 12.5 Hz (8 ms pulse) and 25 Hz (4 ms pulse) AEM data in an area of high conductance using conductivity depth sections to map water depth and seafloor resistivity, and to compare B-field and dB/dt responses. CDI processing of the data with EMFlow software showed that for this dataset, CDIs based on the B-field are better suited for high-conductance targets than CDIs based on the dB/dt data. Water depth was best resolved using 25 Hz B-field data. Sub-sea material and deeper water depths below 45 m was best resolved with 12.5 Hz Bz field data. The 25 Hz dBz/dt data gave the poorest results, due to changes in the bird position not being accounted for in the CDI algorithm.

Conductivity Depth Imaging (CDI) for step response airborne electromagnetic (AEM) data was first described by Macnae et al (1990) . In this process, AEM data are transformed into a (smoothed) conductivity-depth section under an assumption that the physical properties (geology) are quasi-layered. While the CDI method of Macnae and Lamontagne (1987) is only applicable to step response data, other authors such as Eaton and Hohmann, (1989), Polzer (as described in West and Bailey, 1989) and Nekut(1987) have developed modifications suitable for impulse rather than step response data. Since then, a further mathematical development has allowed the CDI method to be applied to arbitrary waveform EM data in some limited cases; most notably the Geotem and Questem commercial airborne EM systems which use an approximately half-sinusoidal transmitter waveform with significant off-time.

The new aerogeophysical equipment of the Geological Survey of Finland

Airborne electromagnetic (EM) methods are often used as a reconnaissance tool in mineral exploration. With the improvement of airborne transient EM (TEM) systems and interpretational techniques, more and more quantitative information can be obtained from airborne EM data. Thus we pose the question is it possible to develop airborne TEM technology to be as effective in orebody detection as ground TEM techniques in the future? Recently, we developed a fast approximate solution of transient EM response to a thin target buried under a thin conductive overburden. Using this solution, the signal-to-geological-noise ratios are computed for an airborne and a ground moving-loop configuration over a range of values of overburden conductance, target conductance and target depths. The results indicate that airborne TEM systems have potentially the same resolving capability as low or moderate power ground TEM systems for discrete targets. The key design features required to achieve this potential capability in airborne TEM systems are large system bandwidth (i.e. range of time windows) and large transmitter moment.

ABSTRACTPermafrost is ubiquitous at high latitudes, and its thickness is controlled by important local factors like geothermal flux, ground surface temperature and thermal properties of the subsurface. We use airborne transient electromagnetic resistivity measurements to determine permafrost thickness on the coast of Ross Island, Antarctica, which contains the active volcano Mt Erebus. Here, resistivity data clearly distinguish resistive permafrost from the electrically conductive fluid-saturated materials underlying it. For our study, we define permafrost as frozen material with a resistivity > 100 Ω·m; more conductive material contains a significant fraction of water or (more likely) brine. We observe that permafrost is very thin near the coast and thickens within several hundred metres inland to reach depths that are typically within the range of 300–400 m. We attribute the sharp near-shore increase in permafrost thickness to lateral heat conduction from the relatively warm ocean, possibly combined with seawater infiltration into the near-shore permafrost. We validate this result with a two-dimensional heat flow model and conclude that away from the thermal influence of the ocean, the local geothermal gradient and heat flux are about 45 ± 5 °C/km and 90 ± 13 mW/m2, respectively. These values are in line with published estimates in the vicinity of Mt Erebus and within the actively extending Terror Rift, but do not reflect a strong heat flow anomaly from volcanic activity of Mt Erebus. Measurements made previously in the McMurdo Dry Valleys, on the other side of McMurdo Sound, tend to be a few dozens of mW/m2 lower, likely reflecting its different tectonic setting on the uplifted rift shoulder of Transantarctic Mountains. Our study demonstrates a new approach towards constraining geothermal flux in polar regions using airborne electromagnetic (AEM) data that can be relatively efficiently collected on regional scales where ice coverage does not exceed the penetration limits of the AEM device, which for the device used is ∼ 500 m under the favourable conditions in the study area.

Newly developed airborne geophysical data processing methods and instrumentation have been applied in a study of the Effie-Coon Lake area of northern Minnesota. The objective of this study is to demonstrate application of emerging technology to mineral resource evaluation of areas where bedrock is largely concealed by glacial cover. Total magnetic field data were processed with recently developed methods to enhance interpretation of bedrock lithology. Airborne electromagnetic (AEM) instrumentation developed by the USGS was flown along with a standard airborne magnetic sensor. Very Low Frequency (VLF) measurements at about 20 kHz have been used to make an apparent resistivity map. This map can be used to interpret depth to bedrock and conductivity variations of the glacial cover. A recently developed AEM system measures signals radiating from powerlines at 60, 180, and 300 Hz. The 60 Hz data are least susceptible to screening by the glacial drift even where it is over 100 m thick. Apparent resistivities have also been computed from these measurements. Bedrock conductive zones, identified by these data, correlate well with commercial ground and AEM survey data. However, there is some suggestion that the conductive zone may be somewhat larger than what was previously mapped.

The passage of the Sustainable Groundwater Management Act in California has highlighted a need for cost-effective ways to acquire the data used in building conceptual models of the aquifer systems in the Central Valley of California. One approach would be the regional implementation of the airborne electromagnetic (AEM) method. We acquired 104 line-kilometers of data in the Tulare Irrigation District, in the Central Valley, to determine the depth of investigation (DOI) of the AEM method, given the abundance of electrically conductive clays, and to assess the usefulness of the method for mapping the hydrostratigraphy. The data were high quality providing, through inversion of the data, models displaying the variation in electrical resistivity to a depth of approximately 500 m. In order to transform the resistivity models to interpreted sections displaying lithology, we established the relationship between resistivity and lithology using collocated lithology logs (from drillers' logs) and AEM data. We modeled the AEM response and employed a bootstrapping approach to solve for the range of values in the resistivity model corresponding to sand and gravel, mixed coarse and fine, and clay in the unsaturated and saturated regions. The comparison between the resulting interpretation and an existing cross section demonstrates that AEM can be an effective method for mapping the large-scale hydrostratigraphy of aquifer systems in the Central Valley. The methods employed and developed in this study have widespread application in the use of the AEM method for groundwater management in similar geologic settings.