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Towards user friendly data-driven minerals exploration: lithological mapping in an orogenic gold setting
Content Provider | Semantic Scholar |
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Author | Kuhn, Stephen Cracknell, Matthew J. Reading, Anya M. Roach, Michael |
Copyright Year | 2015 |
Abstract | Geophysical and remote sensing methods are routinely used to supplement geological observations. They are also of value where geological observations are not available due to a thick regolith profile or where ground access is restricted. Dealing with these large, disparate datasets poses an increasing challenge and opportunity, to the mineral exploration industry. Machine learning algorithms present an efficient, data-driven means of adding value to these data through semi-automated lithological mapping. |
File Format | PDF HTM / HTML |
Alternate Webpage(s) | https://www.segweb.org/SEG/_Events/Conference_Archive/2015/Conference_Proceedings/files/pdf/Poster-Presentations/Abstracts/P230-Kuhn.pdf |
Language | English |
Access Restriction | Open |
Content Type | Text |
Resource Type | Article |