f Regolith Mapping and Characterisation with AEM - A Review

Regolith materials, which have been described as covering “Everything from fresh rock to fresh air”, exhibit marked contrasts in conductivity that have a profound effect on electromagnetic response. Lateral variations within the regolith can produce spatially coherent responses in EM data, and although those seeking deep targets may regard such characteristics as noise, they can be used to advantage when this information is used with other exploration technologies. Horizontal and vertical changes in conductivity may reflect changes in material type, the influence of bedrock and structure on regolith development, and groundwater condition. This information has significance from an exploration perspective, as it may help constrain geochemical sampling strategies and the interpretation of multi-element geochemical and hydrogeochemical data in regolith dominated terrains. In the last decade, significant developments in EM technology have occurred, particularly with helicopter time domain EM systems. Improvements in signal and a reduction in noise have presented the opportunity to map variability much nearer surface, and to map regolith characteristics and variations in considerably more detail to depth. These improvements, although allowing detection of conductors at greater depths, have resulted in systems also becoming sensitive to superparamagnetic (SPM) effects that may be induced by the concentration of maghemite gravels through geomorphic processes accumulating in or adjacent to palaeochannels in transported regolith. Recently advances with fixed-wing TDEM systems have afforded the opportunity to map regolith variability at regional scales and increasingly AEM data from these systems is being viewed as another part of the pre-competitive data suite offered by government agencies. Being able to measure system geometry and account for it when processing the data enables us to resolve variations in regolith cover sequences more accurately at a range of scales. Accompanying hardware and system developments has been the parallel development of robust and stable processing and inversion algorithms, that permit the more rigorous interpretation of data being acquired by airborne systems. Increased computational capabilities allow for the better definition of variations in individual transients. In some instances we can re-sample transients to emphasise variations in parts of a regolith cover sequence, depending on target needs and requirements. We also have the capability to better constrain our data through the incorporation of, for example, borehole (point) information or spatial knowledge provided by lithology, structure and groundwater character. Computation power has provided impetus to better characterise EM systems so that we can better model ground conductivity variation. It also encourages the ability to more efficiently explore model space and analyse the response of alternative models, the hallmark of Bayesian inversion approaches. The output of this type of analysis are probabilities of models given data, noise and any prior information. Although there is now a greater appreciation of AEM technologies having relevance for the study of regolith variability, in part, linked to our ability to sample more quickly and at a higher resolution, making it much easier to map variations in the near surface, there remains a need to better understand the regolith constraints on observed EM response. Until more recently we have been inclined to view regolith as a lumped entity, specifically something that is commonly conductive and something that masks potential targets at greater depth. Arguably, with the noted advances in technology, we are now better placed to define the exploration significance of regolith characteristics that can be resolved by AEM systems.