Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario, Canada

Maepa, Francisca M.; Smith, Richard S.; Tessema, Abera

doi:10.1016/j.oregeorev.2020.103968

Cited by 53 publications

(28 citation statements)

References 74 publications

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“…The latter is based upon the multilayer artificial neural networks. As presented in Figure 4c, deep learning models have been used in 36% of the reviewed papers from which 21% solely utilized deep learning, whereas the remainder employed both deep learning and conventional machine learning methods to choose the best [49,54,58,63,66,70,96]. It is worth noting that the superiority of an approach depends on the application.…”

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

“…To integrate and handle such large datasets, special tools are required. One such tool is the ML, which is well suited and proved to be promising for tackling the problem of mapping geochemical anomalies [45][46][47] and mineral prospectivity, due to its ability to effectively integrate and analyze large geoscience datasets [48][49][50][51][52][53]. ML and AI are actively used for mining complex, high-level, and nonlinear geospatial data and for extracting previously unknown patterns related to geological processes [45].…”

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

“…ML and AI are actively used for mining complex, high-level, and nonlinear geospatial data and for extracting previously unknown patterns related to geological processes [45]. These techniques were applied in the identification of mineralization related geochemical anomalies in China [45,47,[54][55][56], as well as generating a prospectivity map for targeting gold mineralization in Canada [49,50] and China [51], for the detection of iron caps in Morocco [57], for creating a continuous mineral systems model for chromite deposits in Iran [58], and geological mapping studies using the characteristics of rocks [59,60]. The authors in [61,62] integrated multi-sensor remote sensing techniques such as drone-borne photography and hyperspectral imaging for processing with ML algorithms in order to generate the geological mapping.…”

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

“…Therefore, it becomes of utmost importance to predict, relatively accurately, regions with higher potentials for new deposits based on the large datasets of various types of measurements. The dataset can contain lithogeochemical [49,82], spatial [49,50], geochemical [45,55,81,83], geophysical [81], geological [81], concentration of indicator elements [47,51,52,54,56,65,68], hyperspectral [57,60,61], spatial proxies [58], total magnetic intensity [52], isostatic residual gravity [52] data. It is worth emphasizing that in most of such studies mineralogical analyses results are either used to generate the input features for the ML models or ground truth for training such models.…”

Section: The Main Resaons Behind Using ML In the Selected Studiesmentioning

confidence: 99%

“…The lithogeochemical data of sandstones from diamond drill cores [82] and lithogeochemical major oxide data from the Swayze greenstone belt [49] have been used for the identification of sandstones above blind uranium deposits through an ML technique in a first case and for modeling of orogenic gold prospectivity mapping by deploying a support vector machine and an artificial neural network in the second one.…”

Section: The Outputs Predicted/modeled Using ML In the Selected Literature And The Inputs Utilizedmentioning

confidence: 99%

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A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

Section: The Main Resaons Behind Using ML In the Selected Studiesmentioning

confidence: 99%

Section: The Outputs Predicted/modeled Using ML In the Selected Literature And The Inputs Utilizedmentioning

confidence: 99%

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A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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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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Effectiveness of Neural Kriging for Three-Dimensional Modeling of Sparse and Strongly Biased Distribution of Geological Data with Application to Seafloor Hydrothermal Mineralization

Koike

Yono²,

Sá

et al. 2022

Math Geosci

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Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario, Canada

Cited by 53 publications

References 74 publications

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

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

Effectiveness of Neural Kriging for Three-Dimensional Modeling of Sparse and Strongly Biased Distribution of Geological Data with Application to Seafloor Hydrothermal Mineralization

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