Prospectivity mapping
In today's article we are going to delve into the fascinating world of Prospectivity mapping. We will explore its origins, its evolution over time, its impact on current society and its possible future implications. From its inception to its current state, Prospectivity mapping has played a crucial role in various aspects of human life, and it is important to understand its importance to better understand the world around us. Throughout this article, we will examine Prospectivity mapping from different perspectives and offer a detailed analysis of its influence in today's world. Get ready to embark on a journey of discovery and learning about Prospectivity mapping!
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Prospectivity mapping, also known as mineral prospectivity mapping or mineral potential mapping, defines a process used to make better use of mineral exploration data. Geological and geophysical datasets, such as lithological, structural and topographical maps, aeromagnetic, gravity and radiometric imagery are the typical datasets used in the construction of prospectivity maps.
There are two main approaches to prospectivity mapping: data-driven and knowledge-driven.
In areas with significant known mineralisation, a data-driven approach can be adopted in which known deposits are analysed in relation to the surrounding geology. A number of parametric and non-parametric statistical tests can be used to determine if identified spatial relationships are considered statistically significant. Important relationships are then spatially quantified over the entire region of interest. Ultimately multiple quantified relationships are combined, typically using a geographic information system (GIS) into a single prospectivity map.
In areas with little known mineralisation, a knowledge-driven approach can be implemented in which a mineral-systems approach is used in which theories about the formation of the deposit are identified, spatially quantified and then combined using a GIS.
Prospectivity mapping can be constructed using a mix of data-driven and knowledge-driven components and these are often referred to as hybrid prospectivity maps.
Further reading
- Skabar, A., 2003. Mineral Potential Mapping Using Feed-Forward Neural Networks. Proceedings of the International Joint Conference on Neural Networks, vol. 3, 1814-1819. DOI: 10.1109/IJCNN.2003.1223683
- Nykänen, V. and Juhani Ojala, V., 2007. Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield, Finland. Natural Resources Research, Springer Netherlands, vol. 16(2),85-92. DOI: 10.1007/s11053-007-9046-5.
- Nykänen, V., 2008. Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. Natural Resources Research, Springer Netherlands, vol. 17(1), 17-48. DOI: 10.1007/s11053-008-9062-0.
- Leite, E. P. and Souza Filho, E. P., 2009. Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil. Geophysical Prospecting, Published Online: Jan 7 2009 5:22AM, EAGE. DOI: 10.1111/j.1365-2478.2008.00779.x