Measurements with electric and electromagnetic methods on the Earths surface have a limited capability of resolving the conductivity structure of the ground. Thin layers that are not individually resolved will cause an apparent anisotropy - macro anosotropy - of an interpreted layer.
Traditionally, frequency and time domain airborne electromagnetic (AEM) systems have been used for mineral prospecting, often in parts of the world with little environmental electromagnetic disturbance. However, the increased focus on hydrogeological investigations in many parts of the world has led to a growing interest in the possibility of using airborne systems for such purposes, even in densely populated areas (Sengpiel and Siemon 1998). This raises many interesting questions as to the resolution capabilities of AEM systems and their sensitivity to disturbing influence from ambient electromagnetic noise, and the presence of man-made structures such as power lines, buried cables, and fences.
Traditionally, frequency and time domain airborne electromagnetic (AEM) systems have been used for mineral prospecting, often in parts of the world with little environmental electromagnetic disturbance. However, the increased focus on hydrogeological investigations in many parts of the world has led to a growing interest in the possibility of using airborne systems for such purposes, even in densely populated areas (Sengpiel and Siemon 1998). This raises questions about the resolution capabilities of AEM systems and their sensitivity to disturbing influence from ambient electromagnetic noise and the presence of man-made structures such as power lines, buried cables, and fences. The data quality of earlier AEM systems was such that they were mainly used as "bump detectors" capable of indicating the presence of good conductors. Quantitative interpretation of the data was often not warranted (Huang and Fraser 1999). Traditionally, helicopterborne frequency-domain electromagnetic (HEM) data have been processed to produce iso-resistivity maps using lookup-tables, and transformations of the apparent resistivity and the centroid depth obtained from the table lookup have been used to produce pseudosection images. With the general improvement of HEM systems quantitative interpretation has become an option (Sengpiel and Siemon 1998). In general, the objectives of a hydrogeophysics survey are; detection of non-permeable boundaries of a potential aquifer, often coinciding with the clay-sand boundaries, discernment of internal structure in the aquifer and mapping of near-surface capping clays reducing the vulnerability of the aquifer. This paper presents quantitative analyses using one-dimensional (1D) models of the resolution capabilities of a modern HEM system with 5 frequencies. Because a frequency domain ground system equivalent to the HEM system does not exist we have chosen a profile oriented, multi-electrode DC geoelectrical system (CVES) with a comparable depth penetration and compared the resolution capabilities of the HEM system to that of the CVES system for a number of hydrogeologically relevant models.
ABSTRACT The physical parameter derived from the inversion of electromagnetic surveys, the distribution of subsurface conductivity, is interesting in itself only in very few instances. In most cases, the conductivity distribution will have to be interpreted in terms of the target properties of the survey, for example: a geological interpretation of lithology; a hydrogeological interpretation of hydraulic conductivity; a biohazard/geotechnical interpretation of polluted/not‐polluted ground; and/or an archaeological interpretation of manmade/natural finds. The parameters of interest in these categories are often called derived products, indicating that the parameter of interest is not the same as the parameter whose distribution is found in the inversion process of the geophysical data. The interpretation process can be done in a wide variety of ways; from a predominantly cognitive approach based on professional experience, to an application of rigorous quantitative relations found from scientific endeavours. In most practical situations, the number of locations with independently measured information on the derived product is considerably smaller than the number of geophysics locations. It is precisely this sparsity of primary information on the derived product that encourages the use of geophysical inversion results as a sort of qualified interpolator through a formulation of a correlation between a geophysical parameter and the parameter characterising the derived product. In this paper, a general, quantitative approach to deriving the parameter of interest is presented using statistical analytic measures and an advanced use of an interpolation method that takes uncertainties into account. The approach is demonstrated in a field example from Ølgod, Denmark, where the cumulated clay thickness in the upper 30 m is estimated using a combination of borehole drilling records and an airborne transient survey.
We analyse and compare the resolution improvement that can be obtained from including x-component data in the inversion of AEM data from the SkyTEM and TEMPEST systems. Except for the resistivity of the bottom layer, the SkyTEM system, even without including x-component data, has the better resolution of the parameters of the analysed models.
In Australia’s semi-arid and arid interior, groundwater resources provide water supply security for agriculture and community consumptive use and are critical for underpinning economic development. . The Southern Stuart Corridor Project in central Australia, is an inter-disciplinary study which aims to better characterise regional groundwater systems and identify the location, quantity and quality of new groundwater resources. The main aims of the project are(1) to de-risk investment in development of a potential agricultural precinct in the Western Davenport Basin, and expansion of horticulture in Ti-Tree Basin, (2) to identify future water supplies for Alice Springs and Tennant Creek, and (3) for regional water supplies for mineral resource development.The project is funded by Geoscience Australia (GA) as part of the Exploring for the Future (EFTF) Programme. The project integrates airborne electromagnetic (AEM), ground geophysics (ground magnetic resonance (GMR) and borehole geophysics (Induction, gamma and nuclear Magnetic Resonance (NMR)) with drilling and pump testing; hydrochemistry and geochronology; and geomorphic, geological, hydrogeological and structural mapping and modelling. Advancements in temporal remote sensing technologies for surface hydrology, vegetation and landscape mapping are also used to facilitate the identification of recharge and discharge zones and groundwater-dependent vegetation.This paper reports on initial AEM inversion results for the Alice Springs, Ti-Tree Basin, Western Davenport and Tennant Creek areas and the use of a machine learning approach for rapid geological and hydrogeological interpretation of the AEM data. These machine learning approaches have the potential to significantly reduce interpretation time and facilitate the rapid delivery of project results.
Interpretation of a hydrogeophysical survey is a complex and comprehensive process. In addition to an areal coverage with AEM data, most often an interpretation involves additional data that are time consuming to collect and complicated to integrate into an overall model, e.g. borehole logs, borehole core samples, water chemistry, surface vegetation, satellite imagery plus the generally accepted geological background knowledge. Compared with the complexities of the interpretation process, the acquisition, QC and inversion of AEM survey data are a more straightforward affair and considerably less time consuming.Interpretation basically has to do with identifying categories and finding boundaries between them so that depths, thicknesses, lithologies and a whole range of other model attributes can be estimated, qualitatively and quantitatively. To supplement the traditional product delivered by the inverter to the interpreter: inversion models displaying the distribution of subsurface electrical conductivity, I present two methods based on the Continuous Wavelet Transform that can deliver more focused attributes to assist in the interpretation. In the first method, layer boundaries in the smooth multi-layer models that are most often used in the inversion of large data sets are found. In the second method, the spatial distribution of the natural categories of the model parameter is found. Both methods are based on the inversion models and, evidently, they are useful to the extent that the variations in conductivity reflect geological/hydrogeological boundaries and categories - which is for the interpreter to decide.
Electrical and electromagnetic profiling methods are used extensively in environmental geophysical investigations for many different purposes. The pulled array continuous electrical sounding (PACES) method, where a tail of electrodes is towed behind a small vehicle while continuously and simultaneously measuring several electrode configurations, has been used extensively for mapping the vulnerability of aquifers in Denmark. Measurements are taken every 1 m, and 10–15 km of profile can be achieved in one day. This paper presents a theoretical study of the resolution capabilities of PACES measurements as they are now performed, and an experimental design study for including an inductive source in the measuring equipment. The joint interpretation of the galvanic data set of ordinary PACES measurements with inductive data from a horizontal magnetic dipole source will enhance the resolution capabilities of the data set significantly. The study is carried out as an analysis of the uncertainty of the model parameters of one‐dimensional three‐layer models using the estimation error variances of the inversion problem. The results indicate that the addition of only two frequency data from a magnetic dipole source will substantially improve the resolution of the subsurface resistivity structure. The improvement is model dependent, but reduction in the relative error of model parameters by an order of magnitude is observed.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
