The people, science and technology behind discovery

Automated image analysis targets ground selection

by Dan Zlotnikov on June 5, 2012 applied

Application of aeromagnetic structural analysis techniques for greenfields gold exploration.

Ground selection is a crucial stage in every greenfields exploration program. This process frequently relies on data acquired from State or Federal Government organizations seeking to encourage exploration activity; many jurisdictions offer their databases to the public at zero or very low cost.

The data thus acquired can come in a variety of forms, but one type commonly available is the result of aeromagnetic surveys. Conducted from the air and able to “see” through vegetation and snow cover, these surveys can reach remote areas at a relatively low cost. Aeromagnetic surveys are particularly beneficial for gold exploration because geology that favours the formation of gold deposits is frequently also associated with changes in rock magnetism. This makes aeromagnetic data particularly useful to gold explorers, but analysis of considerate volume of magnetic data requires significant time and effort in looking for areas of interest.

The analysis of the data is usually performed manually, with a geologist reviewing the maps and seeking to identify potentially gold-bearing areas. When the data cover millions of square kilometres of surveyed land, the process is not only complex but also extremely tedious. Fortunately, tedious, quantitative analysis of large volumes of data is a prime candidate for automation.

Automating the process is exactly what Eun-Jung Holden, a co-leader of geophysics and image analysis and Associate Professor at the Centre for Exploration Targeting (CET) within the University of Western Australia and her colleagues aimed to do. Their paper Identifying structural complexity in aeromagnetic data: An image analysis approach to greenfields gold exploration, published in Ore Geology Reviews in 2012, details the process and the results of two field applications of the CET Grid Analysis techniques.

The study targeted Archean orogenic-gold deposits formed during the Archean eon, some 3.8 to 2.5 billion years ago. These deposits are located in areas where tectonic plate activity during that period produced mountains. A side-effect of the tectonic upheaval was to break up the solid rock and form channels and cavities into which gold-bearing fluid could rise. Although the fluids are long gone, the gold remains. Today, Archean orogenic deposits represent one of the most important types of gold mineralization, accounting for roughly 20% of global gold production.

Besides being a major source for gold, Archean orogenic-type deposits are also excellent targets for magnetic surveying. The same fluid that brought up gold also served to remove magnetite ores in the vicinity, and on aeromagnetic data, the fluid channels show up as distinct lines or lineaments of diminished magnetism.

Algorithms intended to automate the search for such linear features have been proposed before; the innovation this study advances lies in the combination of an automated search for lineaments and for what is known as “structural complexity.” Simply put, the more lineament contacts  there are in vicinity and less parallel  they are to each other, the more structurally complex an area is considered to be. Such areas have been known to correlate with the location of gold deposits, making areas of higher complexity that much more attractive for further investigation.

The CET team applied their technique to two areas known for Archean orogenic gold discoveries in the Eastern Goldfields Superterrane of Western Australia and in the Abitibi greenstone belt in Ontario, Canada. Both are mature, gold-producing areas with many known deposits, which allowed the researchers to compare the results of the automated analysis with existing maps of deposits.

The automated approach has shown itself well in the testing. “Our experimental results demonstrate there is a high correlation between known mineralisation and the regions of structural complexity that are generated by the proposed method,” Holden and her co-authors conclude. They also point out that the same analysis technique can be applied to other inputs, such as traces from a digital geologic map and for other types of deposits.

Holden acknowledges a number of limitations of the technique. The approach cannot identify the time of formation of a particular lineament, ranking ones that formed at the same time as gold mineralization and ones that appeared after (and are not helpful to mineral explorers). The paper also points out, “Mineral exploration decisions are always based on a combined analysis of multiple datasets, typically geology, geophysics, geochemistry and satellite remote sensing. Our structural complexity maps should  complement the other datasets used in the exploration decision-making process.”

Source: Holden, E.-J., et al., Identifying structural complexity in aeromagnetic data: An image analysis approach to greenfields gold exploration, Ore Geol. Rev. (2012), doi:10.1016/j.oregeorev.2011.11.002.