State-of-the-art analysis of geochemical data for mineral exploration
Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and...
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ftanucanberra:oai:openresearch-repository.anu.edu.au:1885/275597 2023-05-15T17:10:57+02:00 State-of-the-art analysis of geochemical data for mineral exploration Grunsky, E C de Caritat, Patrice 2022-10-18T04:07:50Z application/pdf http://hdl.handle.net/1885/275597 https://openresearch-repository.anu.edu.au/bitstream/1885/275597/3/State%20of%20the%20art.pdf.jpg en_AU eng Geological Society Publishing House 2041-4943 http://hdl.handle.net/1885/275597 https://openresearch-repository.anu.edu.au/bitstream/1885/275597/3/State%20of%20the%20art.pdf.jpg © 2019 The authors http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution licence CC-BY Geochemistry: Exploration, Environment, Analysis https://pubs.geoscienceworld.org/geea/article/20/2/217/571599/State-of-the-art-analysis-of-geochemical-data-for geochemistry analytical methods compositional data multivariate analytics process discovery process validation predictive mapping machine learning geospatial coherence Melville Peninsula Nunavut Thomson Region New South Wales Journal article 2022 ftanucanberra 2022-10-24T22:15:28Z Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyse and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc-related Cu-Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with a similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. The National Geochemical Survey of Australia project was supported by Commonwealth funding ... Article in Journal/Newspaper Melville Peninsula Nunavut Australian National University: ANU Digital Collections Canada Melville Peninsula ENVELOPE(-83.999,-83.999,68.001,68.001) Nunavut |
institution |
Open Polar |
collection |
Australian National University: ANU Digital Collections |
op_collection_id |
ftanucanberra |
language |
English |
topic |
geochemistry analytical methods compositional data multivariate analytics process discovery process validation predictive mapping machine learning geospatial coherence Melville Peninsula Nunavut Thomson Region New South Wales |
spellingShingle |
geochemistry analytical methods compositional data multivariate analytics process discovery process validation predictive mapping machine learning geospatial coherence Melville Peninsula Nunavut Thomson Region New South Wales Grunsky, E C de Caritat, Patrice State-of-the-art analysis of geochemical data for mineral exploration |
topic_facet |
geochemistry analytical methods compositional data multivariate analytics process discovery process validation predictive mapping machine learning geospatial coherence Melville Peninsula Nunavut Thomson Region New South Wales |
description |
Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyse and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc-related Cu-Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with a similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. The National Geochemical Survey of Australia project was supported by Commonwealth funding ... |
format |
Article in Journal/Newspaper |
author |
Grunsky, E C de Caritat, Patrice |
author_facet |
Grunsky, E C de Caritat, Patrice |
author_sort |
Grunsky, E C |
title |
State-of-the-art analysis of geochemical data for mineral exploration |
title_short |
State-of-the-art analysis of geochemical data for mineral exploration |
title_full |
State-of-the-art analysis of geochemical data for mineral exploration |
title_fullStr |
State-of-the-art analysis of geochemical data for mineral exploration |
title_full_unstemmed |
State-of-the-art analysis of geochemical data for mineral exploration |
title_sort |
state-of-the-art analysis of geochemical data for mineral exploration |
publisher |
Geological Society Publishing House |
publishDate |
2022 |
url |
http://hdl.handle.net/1885/275597 https://openresearch-repository.anu.edu.au/bitstream/1885/275597/3/State%20of%20the%20art.pdf.jpg |
long_lat |
ENVELOPE(-83.999,-83.999,68.001,68.001) |
geographic |
Canada Melville Peninsula Nunavut |
geographic_facet |
Canada Melville Peninsula Nunavut |
genre |
Melville Peninsula Nunavut |
genre_facet |
Melville Peninsula Nunavut |
op_source |
Geochemistry: Exploration, Environment, Analysis https://pubs.geoscienceworld.org/geea/article/20/2/217/571599/State-of-the-art-analysis-of-geochemical-data-for |
op_relation |
2041-4943 http://hdl.handle.net/1885/275597 https://openresearch-repository.anu.edu.au/bitstream/1885/275597/3/State%20of%20the%20art.pdf.jpg |
op_rights |
© 2019 The authors http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution licence |
op_rightsnorm |
CC-BY |
_version_ |
1766067621797560320 |