Optimal Thresholding of Predictors in Mineral Prospectivity Analysis
© 2020, International Association for Mathematical Geosciences. Some methods for analysing mineral prospectivity, especially the weights of evidence technique, require the predictor variables to be binary values. When the original evidence data are numerical values, such as geochemical indices,...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
Kluwer Academic/Plenum Publishers
2020
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| Online Access: | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/81939 |
| _version_ | 1848764447944867840 |
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| author | Baddeley, Adrian Brown, Warick Milne, Robin K Nair, Gopalan Rakshit, Suman Lawrence, Tom Phatak, Aloke Fu, Shih Ching |
| author_facet | Baddeley, Adrian Brown, Warick Milne, Robin K Nair, Gopalan Rakshit, Suman Lawrence, Tom Phatak, Aloke Fu, Shih Ching |
| author_sort | Baddeley, Adrian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2020, International Association for Mathematical Geosciences.
Some methods for analysing mineral prospectivity, especially the weights of evidence technique, require the predictor variables to be binary values. When the original evidence data are numerical values, such as geochemical indices, they can be converted to binary values by thresholding. When the evidence layer is a spatial feature such as a geological fault system, it can be converted to a binary predictor by buffering at a suitable cut-off distance. This paper reviews methods for selecting the best threshold or cut-off value and compares their performance. The review covers techniques which are well known in prospectivity analysis as well as unfamiliar techniques borrowed from other literature. Methods include maximisation of the estimated contrast, Studentised contrast, χ2 test statistic, Youden criterion, statistical likelihood, Akman–Raftery criterion, and curvature of the capture–efficiency curve. We identify connections between the different methods, and we highlight a common technical error in their application. Simulation experiments indicate that the Youden criterion has the best performance for selection of the threshold or cut-off value, assuming that a simple binary threshold relationship truly holds. If the relationship between predictor and prospectivity is more complicated, then the likelihood method is the most easily adaptable. The weights-of-evidence contrast performs poorly overall. These conclusions are supported by our analysis of data from the Murchison goldfields, Western Australia. We also propose a bootstrap method for calculating standard errors and confidence intervals for the location of the threshold. |
| first_indexed | 2025-11-14T11:19:31Z |
| format | Journal Article |
| id | curtin-20.500.11937-81939 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:19:31Z |
| publishDate | 2020 |
| publisher | Kluwer Academic/Plenum Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-819392021-11-19T01:15:23Z Optimal Thresholding of Predictors in Mineral Prospectivity Analysis Baddeley, Adrian Brown, Warick Milne, Robin K Nair, Gopalan Rakshit, Suman Lawrence, Tom Phatak, Aloke Fu, Shih Ching Science & Technology Physical Sciences Geosciences, Multidisciplinary Geology Akman-Raftery criterion Capture-efficiency curve Change-point estimation Likelihood Weights of evidence Youden index WEIGHTS-OF-EVIDENCE CHANGE-POINT ESTIMATION OROGENIC GOLD DEPOSITS LOGISTIC-REGRESSION SPATIAL ASSOCIATION MAXIMUM-LIKELIHOOD POISSON-PROCESS YILGARN CRATON INFERENCE TIME © 2020, International Association for Mathematical Geosciences. Some methods for analysing mineral prospectivity, especially the weights of evidence technique, require the predictor variables to be binary values. When the original evidence data are numerical values, such as geochemical indices, they can be converted to binary values by thresholding. When the evidence layer is a spatial feature such as a geological fault system, it can be converted to a binary predictor by buffering at a suitable cut-off distance. This paper reviews methods for selecting the best threshold or cut-off value and compares their performance. The review covers techniques which are well known in prospectivity analysis as well as unfamiliar techniques borrowed from other literature. Methods include maximisation of the estimated contrast, Studentised contrast, χ2 test statistic, Youden criterion, statistical likelihood, Akman–Raftery criterion, and curvature of the capture–efficiency curve. We identify connections between the different methods, and we highlight a common technical error in their application. Simulation experiments indicate that the Youden criterion has the best performance for selection of the threshold or cut-off value, assuming that a simple binary threshold relationship truly holds. If the relationship between predictor and prospectivity is more complicated, then the likelihood method is the most easily adaptable. The weights-of-evidence contrast performs poorly overall. These conclusions are supported by our analysis of data from the Murchison goldfields, Western Australia. We also propose a bootstrap method for calculating standard errors and confidence intervals for the location of the threshold. 2020 Journal Article http://hdl.handle.net/20.500.11937/81939 10.1007/s11053-020-09769-2 English http://purl.org/au-research/grants/arc/IC180100030 Kluwer Academic/Plenum Publishers fulltext |
| spellingShingle | Science & Technology Physical Sciences Geosciences, Multidisciplinary Geology Akman-Raftery criterion Capture-efficiency curve Change-point estimation Likelihood Weights of evidence Youden index WEIGHTS-OF-EVIDENCE CHANGE-POINT ESTIMATION OROGENIC GOLD DEPOSITS LOGISTIC-REGRESSION SPATIAL ASSOCIATION MAXIMUM-LIKELIHOOD POISSON-PROCESS YILGARN CRATON INFERENCE TIME Baddeley, Adrian Brown, Warick Milne, Robin K Nair, Gopalan Rakshit, Suman Lawrence, Tom Phatak, Aloke Fu, Shih Ching Optimal Thresholding of Predictors in Mineral Prospectivity Analysis |
| title | Optimal Thresholding of Predictors in Mineral Prospectivity Analysis |
| title_full | Optimal Thresholding of Predictors in Mineral Prospectivity Analysis |
| title_fullStr | Optimal Thresholding of Predictors in Mineral Prospectivity Analysis |
| title_full_unstemmed | Optimal Thresholding of Predictors in Mineral Prospectivity Analysis |
| title_short | Optimal Thresholding of Predictors in Mineral Prospectivity Analysis |
| title_sort | optimal thresholding of predictors in mineral prospectivity analysis |
| topic | Science & Technology Physical Sciences Geosciences, Multidisciplinary Geology Akman-Raftery criterion Capture-efficiency curve Change-point estimation Likelihood Weights of evidence Youden index WEIGHTS-OF-EVIDENCE CHANGE-POINT ESTIMATION OROGENIC GOLD DEPOSITS LOGISTIC-REGRESSION SPATIAL ASSOCIATION MAXIMUM-LIKELIHOOD POISSON-PROCESS YILGARN CRATON INFERENCE TIME |
| url | http://purl.org/au-research/grants/arc/IC180100030 http://hdl.handle.net/20.500.11937/81939 |