Mineral prospectivity mapping using a VNet convolutional neural network

Abstract

Major mineral discoveries have declined in recent decades, and the natural resource industry is in the process of adapting and incorporating novel technologies such as machine learning and artificial intelligence to help guide the next generation of exploration. One such development is an artificial intelligence architecture called VNet that uses deep learning and convolutional neural networks. This method is designed specifically for use with geoscience data and is suitable for a multitude of exploration applications. One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses gold values from sparse drill holes for training purposes to predict gold mineralization elsewhere in the region. The prospectivity results highlight new target areas, and one such target was followed up with a direct-current induced polarization survey. A chargeability anomaly was discovered wherein the VNet had predicted gold mineralization, and subsequent drilling encountered a 6 g/t Au intercept within 10 m of drilling that averaged more than 1.0 g/t Au. Although most of the prospectivity targets generated from VNet were not drill tested, this first intercept helps validate the approach. We believe this method can help maximize the use of existing geoscience data for successful and efficient exploration programs in the future.

The Design of a Dual Servo Drive and Control Integrated Platform Based on VNet Neural Network

“…e VNet neural network is a new three-dimensional segmentation network based on the FCN network [10]. Generally speaking, a VNet neural network can be understood as a "simplified version" of a feedforward neural network.…”

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

“…Geochemical imaging Qualitative LIBS spectral data PCA [48] K-means [48]; agglomerative hierarchical clustering [48]; GMM [48] Geological and Geophysical Mapping for mineral exploration, mine planning, and ore extraction Multispectral, RGB, and hyperspectral data OTVCA [62] SVM [62] Geological texture classification Images of drill cores GLCM [66]; LBP [66] RF [66]; SVM [66]; k-NN [66]; ANN [66] Identifying and mapping geology andmineralogy on a vertical mine face Hyperspectral data -GP [60] Mapping of gold deposits and prospects Lithogeochemical major oxide data; spatial data PCA [49]; WOE [49] RBFNN [49]; SVM [49] Geoscience data -CNN [50] Geological, geochemical, structural, and geophysical datasets -RBFLN [81] Mineral identification…”

“…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].…”

“…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.…”