Machine Learning in Mineral Exploration: A Tutorial
| dc.contributor.author | Cevik, S. Ilkay | |
| dc.contributor.author | Ortiz, Julian M. | |
| dc.date.accessioned | 2020-07-07T20:24:41Z | |
| dc.date.available | 2020-07-07T20:24:41Z | |
| dc.date.issued | 2019 | |
| dc.description | This is a preprint version of a paper that is or may be under consideration of publication. It does not contain changes and edits that will be made during peer review, or updates, edits and alterations by the authors and the publisher that may occur prior to acceptance and final publication. | |
| dc.description.abstract | This tutorial aims to demonstrate how to conduct some machine learning methods in geoscience to enhance mineral exploration studies. The document does not intent to present an exhaustive list of all the methods available currently, instead it aims to present some useful functions that exist in Python and R to conduct a workflow. The outputs of the procedures are presented hereby, and source codes are available in the notebooks of all the sections which are available on the GitHub page of the research group. They could be downloaded and repeated. | en |
| dc.identifier.citation | Cevik SI, Ortiz JM (2019) Machine Learning in Mineral Exploration: a Tutorial, Predictive Geometallurgy and Geostatistics Lab, Queen’s University, Annual Report 2019, paper 2019-14, 10 p. | en |
| dc.identifier.uri | http://hdl.handle.net/1974/27951 | |
| dc.language.iso | en | en |
| dc.publisher | Queen's University | en |
| dc.relation | Queen’s University Research Initiation Grant | en |
| dc.relation | Mitacs Accelerate | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Machine Learning in Mineral Exploration: A Tutorial | en |
| dc.type | journal article | en |
| oaire.awardNumber | RGPIN-2017-04200 | en |
| oaire.awardNumber | RGPAS-2017-507956 | en |
| oaire.awardNumber | FR37072-IT14666 | en |
| project.funder.identifier | http://dx.doi.org/10.13039/501100003321 | en |
| project.funder.identifier | http://dx.doi.org//10.13039/501100000038 | en |
| project.funder.identifier | http://dx.doi.org/10.13039/501100004489 | en |
| project.funder.name | Queen's University | en |
| project.funder.name | NSERC | en |
| project.funder.name | Mitacs | en |