Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties

Abstract

Mineral prospectivity mapping is an emerging application for machine learning algorithms which presents a series of practical difficulties. The goal is to learn the mapping function which can predict the existence or absence of economic mineralization from a compilation of geoscience datasets (ie: bedrock type, magnetic signature, geochemical response etc). The challenges include sparse, imbalanced labels (mineralization occurrences), varied label reliability, and a wide range in data quality and uncertainty. In order to address these issues an algorithm was developed based on total least squares and support vector machine regression which incorporates both data and label uncertainty into the objective function. This was done without losing sparsity in the residuals, thus maintaining minimal support vectors.Mineral prospectivity mapping is an application for machine learning which presents a series of practical difficulties. The goal is to learn the mapping function which can predict the existence of mineralization from a compilation of geoscience datasets. Challenges include sparse, imbalanced labels, varied label reliability, and a wide range in data uncertainty. To address this, an algorithm was developed based on TLS and SVM which incorporates both data and label uncertainty into the objective function.

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

“…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.…”

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

“…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].…”

“…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.…”

Data driven recovery of local volatility surfaces

“…2. There may be a hidden uncertainty in the locations of data, not only in data values (e.g., [HA08,GH15]). For instance, engineers often prefer to see data given at regular mesh nodes, so a quiet constant interpolation, moving data items to the nearest cell vertex, is common practice.…”