Association Between Imaging and XRF Sensing: A Machine Learning Approach to Discover Mineralogy in Abandoned Mine Voids

Rahman, Ashfaqur; Timms, Greg; Shahriar, Md. Sumon; Sennersten, Charlotte; Davie, Andrew; Lindley, Craig A.; Hellicar, Andrew; Smith, Greg; Holley, Debbie; Coombe, Mac

doi:10.1109/jsen.2016.2546241

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…Classification tasks can be, for instance, determining the type of minerals [73,77,94,95], texture [66,89], or rock [86,93], class of mineral face [86], class of hematite crystals [78], distinguishing between quartz and resin in optical microscopy images [80], presence or absence of specific minerals in a sample [96], zeolite type [97], material fingerprints [98], class of regolith landform [99], and class of carbonates [100] based on a set of measurements or known features about a material. On the other hand, in regression problems, the goal is to estimate a continuous numerical value, for instance, prediction of concentrate yield and modal mineralogy [79] and estimation of drill-core mineral abundance [63], mineral density of elements [101], and calculation of amphibole formula [76].…”

Section: The ML Methods Leveraged In the Selected Workmentioning

confidence: 99%

“…OTVCA [65] RF [63]; SVM [63]; [65]; FF-ANN [63] Estimating the mineralogical compositions Elemental data acquired using X-ray fluorescence (XRF) instruments -ANN [84] Finding association between imaging and XRF sensing Images of rock samples -LR [101]; SVM [101] Generating 2D mineral map of chromite samples Optical micrograph images -RF [90] Geochemical anomaly detection; prospectivity for future exploration Geochemical exploration data; concentration of major and trace elements Feature elimination with cross-validation based on random forest [47]; unsupervised deep belief networks (DBNs) [54]; MMML [55]; hierarchical clustering [56]; SDAE [56]; PCA [56] VAE [45]; RF [47]; CNN [51]; SVM [54]; ACE [55]; IF [56] Litho geochemistry of sandstones obtained from drill cores PCA [82] LDA [82]; RF [82] Geochemical assay (ppm Cu); total magnetic intensity; isostaticresidual gravity -SVM [52] Spatial proxies -ANN [58]; RF [58] Table 3. Cont.…”

Section: General Trends and Research Gaps In The Application Of ML In The Selected Literaturementioning

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: General Trends and Research Gaps In The Application Of ML In The Selected Literaturementioning

confidence: 99%

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

2021

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“…Geological mapping [22][23][24] studies were conducted using the characteristics of rocks. A mineral analysis [25][26][27][28][29][30][31] was conducted using drilling data or samples. A mineral prospectivity modeling and mapping [32][33][34][35][36][37][38][39][40][41][42] study was performed to evaluate the potential of minerals using the exploration data.…”

Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

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“…2) K-nearest neighbor algorithm The KNN algorithm is more intuitive. For a sample in the set to be predicted, calculate the distance from all the samples to this sample in the training set, sort according to the distance, select the closest sample points, and determine the label of the prediction sample according to the number of votes in the category [21][22]. Compared with other machine learning algorithms, the under neighbor algorithm has no explicit modeling process and is a kind of "inert learning".…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Machine Learning Algorithm based on Spatiotemporal Kriging Model in The Construction of National Stock Market Network

2020

IJETC

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Kriging in space-time has obvious advantages in analyzing the continuous change of natural characteristic quantity. This method can use the time correlation to improve the analysis accuracy. In the analysis of stock market network construction, Kriging is still not applied. The purpose of this paper is to construct the national stock market network based on the machine learning algorithm of spatiotemporal Kriging model. Firstly, the basic theory of the basic properties of the network is introduced. Secondly, the principle of machine learning algorithm is further studied. After distinguishing the classical Kriging model and the spatiotemporal Kriging model, the data of stock market network is constructed. Some problems related to the interpolation with spatiotemporal Kriging method are further studied, such as the reasons for the improvement of precision, the selection of database capacity in the process of spatiotemporal interpolation, etc. The experimental results show that the spatiotemporal Kriging method is used to calculate the spatiotemporal variation function, Cst(0,0) is estimated to be 0.062, and the spatiotemporal variation function is constructed. The average path of stock network is 2.0476, which is close to 2.6095 of random network. At the same time, the clustering coefficient of stock market network is 7619, which is much larger than that of random network. For any real network, if it satisfies the following two conditions at the same time, it can be called a small world effect.

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Association Between Imaging and XRF Sensing: A Machine Learning Approach to Discover Mineralogy in Abandoned Mine Voids

Cited by 9 publications

References 19 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

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Machine Learning Algorithm based on Spatiotemporal Kriging Model in The Construction of National Stock Market Network

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