Modeling induced polarization effects in helicopter time-domain electromagnetic data: Field case studies

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.

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.

As an efficient geophysical exploration method, the time-domain airborne electromagnetic (AEM) data often show sign reversal in late-time channels due to induced polarization (IP) effect. The traditional imaging and inversion methods without considering the IP effect cannot recover the true electrical structure of the earth, so it is necessary to develop 3D EM forward modeling and inversion techniques with IP effect. In this paper, we propose a 3D forward modeling method for time-domain AEM with IP effect based on unstructured finite-element (FE) method. To describe the IP effect of a medium, we introduce the Cole-Cole model and transform it into fractional derivative form using frequency-time conversion. Then, we discretize it using the difference discretization format of Caputo fractional derivative. Finally, we use the vector FE method based on unstructured tetrahedral mesh and unconditionally stable second-order backward Euler’s scheme to discretize Maxwell’s equations in space and time. In this way, we can solve the 3D forward modeling problem for AEM with IP effect in time domain. We verify the accuracy of our algorithm by comparing it with the 1D semi-analytical solution for a half-space model, and then calculate EM responses for typical abnormal models and analyze the characteristics of IP effect.

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.

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.

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.

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.

Summary Induced polarization (IP) effects in airborne electromagnetic (AEM) surveys have commonly been investigated in helicopter-borne systems, leaving both a bibliographic and application gap for fixed-wing configurations. This gap partly reflects the large relative number of helicopter compared to fixed wing AEM systems, but also the geometric complexity of fixed wing platforms. In these platforms, nine geometric parameters come into play: the pitch, roll, and yaw of both transmitter and receiver, plus the three-axis offsets between the coils. Shifts in these factors can distort the measured data in ways that aren’t uniquely attributable, making it hard to pinpoint whether negative recordings truly arise from IP or from geometry-related effects. The non-fixed geometry also complicates removal of the primary field, often requiring iterative processing steps that may suppress or alter spectral content linked to IP. With advances in airborne IP understanding from helicopter-borne systems, revisiting fixed-wing platforms is both timely and necessary. Part A of this two-part study addresses this issue using the TEMPEST™ fixed-wing system connecting numerical modelling with field evidence. A suite of synthetic two-layer models with variable resistivity and chargeability parameters was developed to evaluate the system’s sensitivity to polarizable structures. The experiments demonstrate that IP effects, including negative secondary field responses, can be reliably detected in fixed-wing AEM data, both in X and Z magnetic field components. The capacity of these systems to detect IP phenomena is, however, strongly dependent on the electrical conductance of the environment. For instance, both fixed-wing and helicopter-borne systems, elevated near-surface conductance enhances the amplitude of purely electromagnetic induction currents, which in turn can dominate the recorded response and obscure the comparatively weaker polarization currents. More in general, IP detectability depends on the strength of the EM response generated by induction currents flowing elsewhere, which can dominate the small reverse current flow from a polarizable target. This highlights the critical role of near-surface conductivity in controlling the expression of IP responses and underscores the need to carefully account for these factors when interpreting survey data. The synthetic results are then connected with field-scale observations from a subset of the AusAEM dataset, over 470 000 line-km of TEMPESTTM data, where negative responses align with areas of low shallow conductance, confirming the simulation results. These finding open the way to the Part B of this study, where TEMPESTTM data are inverted taking into account IP and compared with helicopter-borne results and geological information.

The new aerogeophysical equipment of the Geological Survey of Finland

In the field of mineral exploration, airborne electromagnetic method is developing important action. Airborne electromagnetic response data obtained by airborne electromagnetic method presents changing trend of attenuation exponential summation, and which has abundant but redundant information character. In order to fast reconstructing airborne electromagnetic response data, Prony Method and Matrix Pencil Method are applied and eigenvector extraction and reconstruction precision of these two methods are comparatively analyzed in this paper. Research results indicate that, eigenvectors extracted by these two methods reflect intrinsic attribution of underground geological body. To non-noise airborne electromagnetic response data, better reconstruction precision realized by these two methods is presented. To including noise airborne electromagnetic response data, when signal to noise ratio gradually decreases from 20 to 0 dB, reconstruction precision of Prony Method rapidly descends, but that of Matrix Pencil Method merely slight fluctuates. Therefore, the latter is more effective to reconstruct airborne electromagnetic response data in complex electromagnetic environment.

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

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.