Supervised Neural Network Targeting and Classification Analysis of Airborne EM, Magnetic and Gamma-ray Spectrometry Data for Mineral Exploration

Karl Kwan; Stephen Reford; Djiba Maïga Abdoul-Wahab; Douglas H. Pitcher; Nasreddine Bournas; Alexander Prikhodko; Geoffrey Plastow; Jean M. Legault

doi:10.1071/ASEG2015ab306

24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery

ISSN: 2202-0586
E-ISSN:

oa Supervised Neural Network Targeting and Classification Analysis of Airborne EM, Magnetic and Gamma-ray Spectrometry Data for Mineral Exploration
Authors Karl Kwan^a, Stephen Reford^b, Djiba Maïga Abdoul-Wahab^c, Douglas H. Pitcher^d, Nasreddine Bournas^e, Alexander Prikhodko^f, Geoffrey Plastow^g and Jean M. Legault^h
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^a Geotech Ltd, 245 Industrial Parkway N. Aurora, , Ontario, , Canada, L4G 4C4, [email protected] ^b Paterson, Grant & Watson Ltd, 155 University Ave. Toronto, , Ontario, , Canada, M5H 3B7, [email protected] ^c CPG/PRDSM 634 Boulevard des Nations Rue YN -34, Porte, 585, République du Niger, [email protected] ^d Geotech Airborne Limited, BE40, Kwaning Chateau Adjiringanor, East Legon Accra, , Ghana, Box 16160, , Kia, [email protected] ^e Geotech Ltd, 245 Industrial Parkway N. Aurora, , Ontario, , Canada, L4G 4C4, [email protected] ^f Geotech Ltd, 245 Industrial Parkway N. Aurora, , Ontario, , Canada, L4G 4C4, [email protected] ^g Geotech Ltd, 245 Industrial Parkway N. Aurora, , Ontario, , Canada, L4G 4C4, [email protected] ^h Geotech Ltd, 245 Industrial Parkway N. Aurora, , Ontario, , Canada, L4G 4C4, [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2015, Issue 24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery, Dec 2015, p. 1 - 5
DOI: https://doi.org/10.1071/ASEG2015ab306
- Published online: 01 Dec 2015

Abstract

The amount of multi-disciplinary (geology, geophysics, remote sensing, etc.) and multi-parameter geophysical (potential field, EM, gamma-ray spectrometry, etc.) data available for mineral exploration is ever increasing. The integration and analysis of the data require effective and efficient search engines or data mining tools. The search engines will take the signatures of known mineral deposits or interpreted mineralization targets (“key words”), search the data space and return potential new targets (“matches”), thus providing locations to the decision makers for follow-up. Two supervised feedforward multilayer neural network (NN) search algorithms will be presented and analysed. The utility of the NN search tools will be demonstrated with the integration and analysis of airborne electromagnetic (EM), magnetic and radiometric data for mineralization targets in Iullemmeden Basin, Niger.

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Article Type: Research Article

Keyword(s): Iullemmeden Basin Niger; mineral exploration; neural network; Search engine

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oa Supervised Neural Network Targeting and Classification Analysis of Airborne EM, Magnetic and Gamma-ray Spectrometry Data for Mineral Exploration

Abstract

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