Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data
Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
M D P I AG
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/73078 |
| _version_ | 1848762917841797120 |
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| author | Kitzig, M. Kepic, Anton Grant, A. |
| author_facet | Kitzig, M. Kepic, Anton Grant, A. |
| author_sort | Kitzig, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate workflows. Grade control is one such issue where the laboratory assay turn-around times cannot beat the shovel. We propose that for iron ore deposits in the Pilbara geophysical downhole logging may provide the necessary and sufficient information about rock formation properties, circumventing any need for real-time elemental analysis entirely. This study provides an example where petrophysical downhole data is automatically classified using a neuro-adaptive learning algorithm to differentiate between different rock types of iron ore deposits and for grade estimation. We exploit a rarely used ability in a spectral gamma-gamma density tool to gather both density and iron content with a single geophysical measurement. This inaccurate data is then put into a neural fuzzy inference system to classify the rock into different grades and waste lithologies, with success rates nearly equal to those from laboratory geochemistry. The steps outlined in this study may be used to produce a workflow for current logging tools and future logging-while-drilling technologies for real-time iron ore grade estimation and lithological classification. |
| first_indexed | 2025-11-14T10:55:11Z |
| format | Journal Article |
| id | curtin-20.500.11937-73078 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:55:11Z |
| publishDate | 2018 |
| publisher | M D P I AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-730782019-01-15T00:37:52Z Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data Kitzig, M. Kepic, Anton Grant, A. Fluctuating commodity prices have repeatedly put the mining industry under pressure to increase productiveness and efficiency of their operations. Current procedures often rely heavily on manual analysis and interpretation although new technologies and analytical procedures are available to automate workflows. Grade control is one such issue where the laboratory assay turn-around times cannot beat the shovel. We propose that for iron ore deposits in the Pilbara geophysical downhole logging may provide the necessary and sufficient information about rock formation properties, circumventing any need for real-time elemental analysis entirely. This study provides an example where petrophysical downhole data is automatically classified using a neuro-adaptive learning algorithm to differentiate between different rock types of iron ore deposits and for grade estimation. We exploit a rarely used ability in a spectral gamma-gamma density tool to gather both density and iron content with a single geophysical measurement. This inaccurate data is then put into a neural fuzzy inference system to classify the rock into different grades and waste lithologies, with success rates nearly equal to those from laboratory geochemistry. The steps outlined in this study may be used to produce a workflow for current logging tools and future logging-while-drilling technologies for real-time iron ore grade estimation and lithological classification. 2018 Journal Article http://hdl.handle.net/20.500.11937/73078 10.3390/min8070276 http://creativecommons.org/licenses/by/4.0/ M D P I AG fulltext |
| spellingShingle | Kitzig, M. Kepic, Anton Grant, A. Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| title | Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| title_full | Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| title_fullStr | Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| title_full_unstemmed | Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| title_short | Near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| title_sort | near real-time classification of iron ore lithology by applying fuzzy inference systems to petrophysical downhole data |
| url | http://hdl.handle.net/20.500.11937/73078 |