Translating a mineral systems model into continuous and data-driven targeting models: An example from the Dolatabad chromite district, southeastern Iran

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

“…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.…”

“…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.…”

“…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.…”

“…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.…”

Structural and non-structural statistical methods: implications for delineating geochemical anomalies

“…The use of exploration geochemistry is paramount in the recognition of blind and buried deposits; that is, exploration geochemistry can reveal mineralisation without surface exposure and under cover (Carranza 2008; Navidi et al 2014). The various techniques in geochemical exploration are applied to recognise mineralisation-related anomalies (Carranza 2008; Cohen et al 2010; Ziaii et al 2009; Grunsky 2010; Ziaii et al 2012; Abdolmaleki et al 2014; Luz et al 2014; Asadi et al 2015; Moeini and Torab 2017; Ziaii et al 2019; Roshanravan 2020; Roshanravan et al 2020).…”