Mineral potential mapping with a restricted Boltzmann machine

Yongliang, Chen

doi:10.1016/j.oregeorev.2014.08.012

Cited by 110 publications

(34 citation statements)

References 61 publications

(74 reference statements)

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“…i.e., the stronger the spatial association is between the discovered polymetallic deposits and the buffered linear features (Chen, 2015;Chen and Wu;.…”

Section: Geological Map Patternmentioning

confidence: 99%

“…Thus, explanatory linear features used for mapping polymetallic prospectivtiy in this study were transformed into areal map patterns using buffer analysis in MapInfo. The Youden index was used to determine the optimal buffering width of linear features by maximizing spatial association between the buffered linear features and the discovered polymetallic deposits (Chen, 2015; Chen and Wu; 2016).…”

Section: Geological Map Patternmentioning

confidence: 99%

“…There is a variety of statistical methods for mineral prospectivity mapping that can be categorized into data-driven, knowledge-driven, and hybrids of them. Data-driven methods are the most popular mineral prospectivity mapping techniques, which are often theoretically based on regression/classification algorithms that come from machine learning field, for example, feed-forward neural networks (Brown et al, 2000(Brown et al, , 2003aHarris and Pan, 1999;Harris et al, 2003;Oh and Lee, 2010;Skabar, 2003), multilayer perceptrons (Chen, 2015;Skabar, 2005Skabar, , 2007, Bayesian networks (Porwal et al, 2006), radial basis functional link net (Behnia, 2007;Leite et al, 2009a;Nykänen, 2008;Porwal et al, 2003), probabilistic neural networks (Leite et al, 2009b), support vector machines (Abedi et al, 2012;Geranian et al, 2016;Zuo and Carranza, 2011), and random forests Laborte, 2015a, b, 2016;McKay and Harris, 2016; A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 6 mineral prospectivity, and (d) the geological features that are critical for polymetallic mineralization and can be used as spatial recognition criterion of polymetallic prospectivity in the study area.…”

Section: Introductionmentioning

confidence: 99%

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Mapping mineral prospectivity using an extreme learning machine regression

Yongliang

2017

Ore Geology Reviews

Self Cite

119

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“…i.e., the stronger the spatial association is between the discovered polymetallic deposits and the buffered linear features (Chen, 2015;Chen and Wu;.…”

Section: Geological Map Patternmentioning

confidence: 99%

Section: Geological Map Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping mineral prospectivity using an extreme learning machine regression

Yongliang

2017

Ore Geology Reviews

Self Cite

119

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“…probability Conducting the W-of-E modeling requires evidence map patterns to satisfy the pairwise conditional independence (CI) assumption [28]. An χ 2 (chi-square) test was used to verify whether the weights-of-evidence layers satisfied the conditional independence assumption.…”

Section: Test and Calculation Of Posteriormentioning

confidence: 99%

“…Mineral exploration is a sophisticated process that seeks to discover new mineral deposits in a region of interest [28]. Mineral prospectivity mapping (MPM) is used as a tool to delineate target areas that most likely contain mineral deposits of a particular type [26].…”

Section: Introductionmentioning

confidence: 99%

Mineral prospectivity mapping with weights of evidence and fuzzy logic methods

Zhang

Zhou

2015

IFS

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Abstract. Knowledge-and data-driven approaches are two major methods used to integrate various evidential maps for mineral prospectivity mapping (MPM). Geological maps, geochemical samples and data from known gold deposits were collected in the western Junggar area, Xinjiang Province. The geological and a spatial database for geological and mineral occurrences were constructed for the studied region. A weights-of-evidence model and a fuzzy logic model were employed for MPM, and the results were compared. Results indicate that favorable sedimentary rocks, fault density, fault distance and concentration of Au were the primary factors affecting Au mineralization. Arsenic (AS), Stibium (Sb), fault direction, quartz veins and intrusive rocks were secondary factors affecting Au mineralization. Conditional independence exerted a major influence on the weights-of-evidence model. However, posterior probability would be very high if the conditional independence was disregarded, which impaired results. Combining the quantification results provided by weights-of-evidence and the fuzzy membership values determined by expert knowledge, the mineral prospectivity mapping according to the fuzzy logic method was proved to be valid. For the study area, which had a large number of deposits, data-driven approaches for MPM are generally considered to be appropriate. However, if sufficient data are not collected, the knowledge-driven approaches, for example, the fuzzy logic method used in the present study, usually achieves a better result.

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Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China

Liu

Yonghang

2022

Preprint

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The current study aimed at assessing the capabilities of five machine learning models in term of mapping tungsten polymetallic prospectivity in the Gannan region of China. The five models include logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Geochemical, lithostratigraphic, and structural datasets were used to generate 16 evidential maps, which were integrated into the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performances of the models were evaluated through receiver operating characteristic (ROC) and K-fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb and Cd, implying that these are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict maximum known deposits within a minimum area, based on the prediction-area plot analysis of the five models, while the RF model can capture the most well-known deposits within the smallest study area. Finally, eighteen prospective areas were delineated according to the predicting results of the machine learning models, which will provide important guidance for further tungsten polymetallic exploration and associated studies.

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Mineral potential mapping with a restricted Boltzmann machine

Cited by 110 publications

References 61 publications

Mapping mineral prospectivity using an extreme learning machine regression

Mapping mineral prospectivity using an extreme learning machine regression

Mineral prospectivity mapping with weights of evidence and fuzzy logic methods

Mineral prospectivity mapping of tungsten polymetallic deposits using machine learning algorithms and comparison of their performance in the Gannan region, China

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