SummaryAirborne electromagnetic data generated by the AusAEM Survey are shown to map mineral deposit host rocks and regional geological features within the AusAEM Survey area. We have developed new functionality in Geoscience Australia’s sample-by-sample layered earth inversion algorithm, allowing inversion of the magnitude of the combined vector sum of the X- and Z-components of TEMPEST AEM data. This functionality improves the clarity of inverted interpretation products by reducing the degree of along-line incoherency inherent to stitched 1D inversions. The new inversion approach improves the interpretability of sub-horizontal conductors, allowing better mapping of geological features under cover.Examples of geological mapping by the AusAEM survey highlight the utility of wide line spacing, regional AEM surveying to improve geological, mineral systems and groundwater resource understanding in the regions flanking outcropping mineral deposit host rocks in northern Australia.
SummaryDissolved salt in aquifers is a potential threat to fresh water resources and the environment. Interpolated grid maps at aquifer depths, derived from borehole EC measurements on water samples, are combined with more detailed bulk electrical conductivities from airborne electromagnetics to provide detailed estimates of total dissolved salt load and subsurface porosity values. A resistive host matrix assumption implies that our calculated porosity and salt load values are maximum values. This mapping technique is a large area coverage remote senor method which has been tested in different areas of the salt-threatened Murray-Darling basin. This technique provides extensive information on the hydraulic properties of aquifers, important for quantitative hydrology.
We aim to develop a quantitative method for recalibration of historic helicopter electromagnetic data sets. Recent research has shown that frequency-domain helicopter electromagnetic data collected over a conductive half-space such as calm seawater can be used to correct system calibration errors. However, most historic surveys consist only of data collected over land, where the conductive half-space assumption is rarely justified. We estimate the required recalibration parameters by analyzing systematic misfits in the inversion of statistically chosen measures of historic data. Our method requires the identification, within the survey area, of a zone of conductive responses that are reasonably uniform. From this zone, a set of altitude-corrected median responses are estimated. These are inverted using geologically specifiedconstraints to obtain a best-fit layered earth model. Systematic inconsistencies between the median measured altitude and the inverted depth to surface are attributed to altitude error. Remaining frequency-dependent fitting errors are assumed to be the calibration errors. We tested the method with partial success on helicopter electromagnetic data sets collected over uniform deep sediments where seawater data were also available and two different inland surveys over multiple lithologies in one general area. At high frequencies, our method works reliably. Recalibration of low-frequency data is not possible if the area used as a reference consists of moderate or poor conductors. In this case, data amplitudes are small and are greatly affected by imperfect drift and magnetic susceptibility corrections. Historic helicopter electromagnetic data may require amplitude rescaling up to 20%–30%, with phase shifts of up to 3°.
SummaryAusAEM is a Geoscience Australia program to collect broad-spaced (~20 km) airborne electromagnetic (AEM) data at the regional scale. The AusAEM data is being used to map the thickness and character of sedimentary and regolith cover across northern Australia. To maximise the utility of the collected data, it is important that subsequent interpretation can integrate the best available ground-control information. Typically such information is provided by boreholes.Prior to AEM data collection, we manually assessed boreholes according to a suite of metadata including spatial location information, depth, quality of lithological information, and the availability of geophysical wireline logging. These assessments are then used to deviate the planned AEM flight lines to intersect high-quality boreholes. However, this process proved prohibitive in the Pilbara; a mature mineral province with extensive drilling. Even after filtering for depth (>50 m), there are ~78,000 mineral exploration boreholes in the current survey area. New methods are clearly required to enable the efficient prioritisation of borehole targets.To this end, we have used the results of previous manual borehole assessments to train and validate a machine learning algorithm for the purpose of identifying priority borehole targets. We find that a detailed manual assessment of boreholes can be closely replicated using a substantially reduced suite of borehole metadata. While the quality of assessment is mildly reduced, and there is a loss of borehole-specific information, the trade-off is a process that is ~137,000 times faster. In practical terms, this has enabled a validated quality assessment to be conducted over an area of extensive drilling, a feat which would have proved prohibitive without machine learning.
SummaryThe majority of airborne electromagnetic (AEM) data is processed using stitched consecutive 1D approximations, from which conductivity depth or CDI sections can be produced. Obtaining calculated variable layer depths from AEM is one of the appealing assets of the method. The current induced by an AEM system in the nearsurface circulates preferentially at a radial distance from the horizontal transmitter (commonly called footprint), the section plotted below the receiver is actually generated from currents induced in the general vicinity and not directly below the reviver-transmitter system. Detection of palaeochannels, faults and other laterally varying structures are common geophysical mapping targets. Caution should be taken when interpreting these horizontally-anisotropic targets; especially if smaller that the system 'Annulus of resolution' or close to edges of discontinuous layering.All AEM systems have different transmitter-receiver geometries, moments, wave forms and others specifics. Forward modelling and conductivity-transforms provide a good method to understand CDI sections and for system comparison. When 2D/3D effects of discontinuities are present, 1D modelling incorrectly predicts weak conductors at depth. Within a conductive layer where conductivity changes with facies or geometrical thickness, 1D approximations work well. 2D processing of 2D data is clearly needed, but existing inversion methods are too slow. 2D/3D structures with abrupt conductivity boundaries resolved with an isotropic layered assumption must always be queried.
SummaryAirborne EM surveys are a well-established method for quickly investigating an area in order to assess what lies beneath the surface. The accuracy of conductivity estimates derived from AEM surveys are negatively impacted by (a) imprecise characterization of the AEM system, (b) miscalibration of data, incorrect estimations on noise levels, (c) assumptions made in the data processing and transformation/inversion to conductivity, and (d) non-uniqueness of the derived models. It is therefore prudent to “validate” the derived models against independent conductivity measurements. Borehole conductivity induction logs are one common source of independent information.When comparing data sets of different origins, such as AEM and down-hole conductivity logging, caution must be used. Immediately evident is the considerations of scale: the bulk conductivity from an airborne survey is affected by large-scale structures such as discontinuous layers and regional faults; whilst borehole skin depth and the conductivity values are affected by casing materials, voids, temperature and calibration. Conductivities measured from boreholes come from an invasive method, where drilling can disrupt the in situ layering and material conditions. All these aspects impact on the conductivity measurements. Airborne data can also be incorrectly processed and interpreted by using incorrect wave forms, frequencies, and altitudes, or by not taking proper care when correcting for instrument drifts.Borehole conductivity logging can successfully reduce the influence of calibration errors in AEM data but boreholes are usually sparsely concentrated and subject to calibration errors of their own. Therefore, the need of a measure of “goodness” in the correlation between data sets is desirable.We suggested a generalisation in the regression analysis to assess the ‘consistency’ and ‘misfit’ between AEM inversion results and borehole data, based on an extension of the Pearson product-moment coefficient of correlation that accounts for errors on both data sets.
An extensive AEM survey recently commissioned by Geoscience Australia involved the use of two separate SkyTEM helicopter airborne electromagnetic (AEM) systems collecting data simultaneously. In order to ensure data consistency between the two systems, we follow the Danish example (conceived by the hydrogeophysics group from Aarhus University) of using a hover test site to calibrate the AEM data to a known reference. Since 2001, Denmark has employed a national test site for all electromagnetic (EM) instruments that are used there, including the SkyTEM system. The Lyngby test-site is recognised as a well-understood site with a well-described layered-earth structure of 5 layers. The accepted electrical structure model of the site acts as the reference model, and all instruments are brought to it in order to produce consistent results from all EM systems. Using a ground-based time-domain electromagnetic (TEM) system which has been calibrated at the Lyngby test site, we take EM measurements at a site selected here in Australia. With sufficient information of the instrument, we produce a layered-earth model that becomes the reference model for the two AEM systems used in the survey. We then bring the SkyTEM systems to the hover site and take soundings at multiple altitudes. From the hover test data and the ground based model, we calculate an optimal time shift and amplitude scale factor to ensure that both systems are able reproduce the accepted reference model. Conductivity sections produced with and without calibration factors show noticeably different profiles.
All helicopter electromagnetic data exhibit effects of bird pendulum. In addition, historic Helicopter EM data are on occasion severely miscalibrated. If the data are not corrected for these effects, then conductivity‐depth images (CDI) or inversions will likely be in error. Recently published methods of recalibration in the dimensionless amplitude‐phase domain and correction for bird swing were applied to an HEM data set collected in 2002 adjacent to and over an island offshore Australia. We show major improvements in the coherency of CDI sections, as well as significant improvements in agreement with geographic observations. A depression in a deep saline conductor is mapped under a swamp thus mapping recharge parameters.
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