Conductivity - depth imaging of airborne EM 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.

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.

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.

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

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

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.

The new aerogeophysical equipment of the Geological Survey of Finland

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.

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.

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

Successful exploration with airborne electromagnetic (AEM) methods depends to a large extent on effective methods for the quantitative interpretation of anomalies caused by variations in the conductivity and thickness of the overburden. Interpretation of AEM data is commonly via one-dimensional (1-D) inversion or the conductivitydepth imaging (CDI) approaches. These procedures however yield incorrect results when actual geological structures are multimensional. The objective of this paper is to assess the limitations of the 1-D inversion methods when applied to the reconstruction of 2-D overburden structures, based on an analysis of the resolution of the conductivity of the weathered layer and the depth to its lower boundary. Simple rules of thumb are derived which may assist in the characterisation of the overburden in geological mapping or environmental investigations, and for bedrock target detection in an area with inhomogeneous conductive overburden using AEM.

As part of a project by the Department of Water Affairs, Botswana to evaluate groundwater resources in the Boteti River area in Botswana airborne EM data were acquired with the TEMPEST system with the objective to map prospective lithologies and structures containing freshwater aquifers. The project area is characterized by a palaeolake system bordered to the south by elevated terrain. Most of the lower-lying area is covered by a thick layer of Kalahari Beds with saline groundwater generally situated less than 20 m below surface. Elevated terrain to the south have Karoo sediments outcropping in places. In the north of the project area AEM data were acquired across palaeolake terrain to locate fresh water zones associated with recharge pans above the saline water table. The derived conductivity structure outlines several shallow, resistive zones that appear to correspond to dry sand; fresh water pockets above the saline water were not located by the boreholes drilled. Further, the AEM results indicate the outline of a brackish to saline aquifer at a depth of 30 m beneath a clay layer. Preliminary borehole data from this aquifer indicate high yields (20-64 m3/h) with TDS values in the range 1550- 2700 mg/l. A comparison of borehole information with the modeled conductivity structure indicates the conductivities to be dominated by clay content, water saturation and salinity. In the southeast of the project area AEM data were acquired across elevated terrain to delineate fresh sandstones sandwiched in-between dry alluvium or basalt and mudstone. The comparison of airborne EM and magnetic data with drillhole information indicates that the basalts are characterized by very low conductivities and a strong magnetic response. The underlying sand- and mudstones have intermediate and high conductivities, respectively, and are characterized by horst-graben structures, reflected in the magnetic data and conductivity-depth sections. Resistivity isosurfaces generated from the 3D conductivity data facilitate the spatial appreciation of the geometry of the interpreted structures. Favorable locations for freshwater exploitation interpreted from the TEMPEST data include a shear zone and sandstone units fully preserved beneath basalt caps. Preliminary borehole data indicate yields of 15- 54 m3/h of brackish to saline water (TDS 1000-3000 mg/l) in the depth range 56-180 m with the highest yield obtained from fractured sandstone at 180 m depth. The results indicate the conductivities of Karoo lithologies to be dominated by clay content, water salinity and, locally, by the presence of quartz.

SummaryConductivity depth data presentations derived using 1D approximations have become a very popular way to present airborne electromagnetic (AEM) data. While data profiles and a single error limit per station can accompany these presentations there are more information contained in data. This work proposes some ideas for augmenting 1D derived data presentations to highlight two and three-dimensional conductors as well as more detailed error level indication. The 1D conductivity depth algorithm used is an extended version of the S-layer differential transform. The depths calculated with the transform is used also present decay constant values corrected for conductive background responses. Fraser filtered X-component data are used to indicate structural complexity and data fit error levels are used to grey out areas on the depth section presentations where the results are less reliable. To demonstrate these ideas a line of Xcite data over the Abra deposit in Western Australia is analysed. The augmentations add valuable information to the conductivity depth section in the sense of drawing attention the where the 1D assumptions are not valid and should encourage interpreters to pursue 2D or 3D modelling techniques for successfully mapping the ore deposit.

We have demonstrated that the induced polarization effect in the airborne electromagnetic (AEM) data can be represented well using the generalized effective-medium theory of induced polarization (GEMTIP) model. We have also developed a novel method for 3D inversion of the airborne spectral induced polarization (SIP) data into the volume distribution of the parameters of the GEMTIP model. The results show that the SIP effect can be observed by a typical AEM system. The AEM data collected by the standard systems can be used not only for producing resistivity models of the subsurface formations, but for recovering the chargeability and other GEMTIP model parameters which can be subsequently used for mineral exploration and possibly mineral discrimination.

Zohdy's method for the inversion of dc-resistivity data has been adapted to the inversion of airborne electromagnetic (AEM) data. AEM responses are first transformed into apparent-conductivity depth profiles, followed by an iterative adjustment of layer thicknesses and interval conductivities. The start model, including the number of layers, is determined from the data. This approach optimizes model flexibility without the need for parameter regularization. Results from Zohdy's inversion applied to TEMPEST, GEOTEM, and [Formula: see text] data acquired in a range of conductivity scenarios including the Bull Creek prospect in Queensland, Australia; the Boteti area, Botswana; and the Reid-Mahaffy test site in Ontario, Canada, show well-delineated target zones. A comparison with Occam's inversion shows good agreement between the conductivity-depth models recovered by the two methods, with Zohdy's inversion being 25 to 80 times faster.