Intelligent Recognition of Ore‐Forming
Different machine learning algorithms have been applied in metallogenic ... The study area is located in the eastern West Qinling orogenic belt in the Daqiao gold-antimony ore exploration area. ... The addition of remote sensing image data causes outliers to converge in the vicinity of the gold ore point 3, the copper and molybdenum …
Mineral exploration using hyperspectral data
Spectral mapping. The ultimate aim of the utilization of hyperspectral data in mineral exploration is to map surface mineralogy proxy of mineral deposits and also to prepare detailed lithological maps delineating host rock and other controls of mineralization. Several mapping algorithms are available to map targets.
Mapping alteration minerals in the Pulang porphyry copper ore …
@article{Zhao2021MappingAM, title={Mapping alteration minerals in the Pulang porphyry copper ore district, SW China, using ASTER and WorldView-3 data: Implications for exploration targeting}, author={Zhifang Zhao and Jiaxi Zhou and Yingxiang Lu and Qi Chen and Xiaoyan Cao and Xiaojia He and Xuexin Fu and Shihui Zeng and Wenqing Feng}, …
Enhancing XRF sensor-based sorting of porphyritic copper ore …
Machine learning techniques, designed to optimize program performance on unobserved data, have been successfully employed in solving engineering problems [13], ... From the results above, for copper ore XRF sorting classification model, it is important to note that LR models have the potential to exhibit overfitting, whereas SVM does not. ...
Liangming LIU | Central South University, Changsha | CSU
The 3D computational modeling-based machine learning (ML) prediction is an innovative methodology for exploration-targeting. ... The Fenghuangshan ore field is a maturely explored skarn copper ore ...
Journal of Geochemical Exploration
Furthermore, with the development of exploration, porphyry ore bodies have also been observed in the ... The analyses were carried out on an Agilent 7900 Quadrupole ICP–MS instrument coupled to a Photon Machine Analyte HE 193-nm ArF Excimer Laser Ablation system. ... Implications for gold and copper exploration. Ore Geol. Rev., 79 (2016), pp ...
Exploration of a Copper Ore Deposit in Elbistan/Turkey …
We present the results of a direct current (DC) resistivity and time-domain induced polarization (TDIP) survey exploring a copper ore deposit in Elbistan/Turkey. The ore deposit is elongated below a valley and is of disseminated form with sulfide content. DC and IP data were acquired using the pole-dipole array on eight parallel profiles crossing …
Innovations: Introduction to Copper: Mining & Extraction
Mining of copper ores is carried out using one of two methods. Underground mining is achieved by sinking shafts to the appropriate levels and then driving horizontal tunnels, called adits, to reach the ore. Underground mining is, however, relatively expensive and is generally limited to rich ores. El Teniente, in Chile, is the world's largest ...
A mineralogy characterisation technique for copper ore in …
The deposit contains copper, iron, lead, zinc sulfides along with gold. Their concentrator has flexibility to produce gold doré (a semi-pure concentrate of gold and silver) and separate copper, lead, and zinc concentrates depending on the stockpile blending. In this study, the chosen processing route was to produce a copper concentrate. 2.1.1.
Mapping alteration minerals in the Pulang porphyry copper ore …
The mineralization of the Pulang porphyry copper deposit (Fig. 2) involved arc magmatism during the late Triassic with the quartz diorite porphyry and quartz monzonitic porphyry (Li and Zeng, 2007).The porphyry copper deposits are mainly formed in the relatively late quartz monzonite porphyry bodies and the industrial ore bodies mainly …
Application of hyperspectral remote sensing for supplementary …
A gold–silver–lead–zinc polymetallic ore was selected in Huaniushan, Gansu Province as the study area. Hyperspectral aerial images as the primary information source, ground spectrum tests ...
Predicting the emplacement of Cordilleran porphyry copper systems using
The use of machine learning methods is becoming increasingly popular in mineral exploration (Farahbakhsh et al., 2020, Shirmard et al., 2021, Shirmard et al., 2020). Moreover, Chandra et al. (2021) use machine learning for spatio-temporal data analysis to reconstruct earth's geological and climate history in deep time spanning …
(PDF) Exploration targeting of copper deposits using staged …
Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco) ... Ore Geology Reviews 143 ...
NAMIBIA'S COPPER POTENTIAL
compatible concentrate processing facility that accommodates copper ores from South America and Europe and exports its blister copper product via the Walvis Bay port. Its by-product, sulphuric acid is ... copper Exploration Resources (02.02.2012)2 total resource: 1.38 Mt @1.055% Cu total measured & indicated: 762, 600 t @1.140% Cu total ...
Explore, discover and analyse: the best mineral testing tools
With results available in a matter of moments, handheld XRF analysers are one option for speeding up exploration and on-site analysis process. XRF is commonly used to map deposits of base and precious metals, as well as rare earth elements and iron ore. Also, XRF analysers are often configurable to maximise their effectiveness in …
Mineral Exploration from Space
Mineral Exploration from Space. Future advances in hyperspectral imagery promise to be a boon for mineral exploration. And while remote sensing technology is improving rapidly, not just any satellite can capture the quality of imagery needed to accurately decide where to look closely for deposits of copper ore, zinc, or other minerals.
Estimating Ore Grade Using Evolutionary Machine Learning …
About this book. This book examines the abilities of new machine learning models for predicting ore grade in mining engineering. A variety of case studies are examined in this book. A motivation for preparing this book was the absence of robust models for estimating ore grade. Models of current books can also be used for the different sciences ...
Introduction to the special issue on spatial modelling and …
Sun et al. (2019) employ several machine learning algorithms, including support vector machine, artificial neural networks and random forest, in their approach to MPM of copper skarn systems in the Tongling ore district, eastern China. As indicated by the model performance statistics, the random forest model outperforms the other models …
Machine Learning Prediction of Ore Deposit Genetic Type
Magnetite geochemistry is crucial for the discrimination of ore deposit genetic type. Traditional two-dimensional discrimination diagrams based on particular data for limited deposits cannot meet the requirements of high-precision classification. The continuous compilation of magnetite geochemical big data and high-precision machine …
Mapping alteration minerals in the Pulang porphyry copper ore …
Section snippets Geological setting. The Pulang porphyry copper deposit is one of the largest porphyry copper deposits in China. It is located at the southern end of the Yidun continental arc, SW China (Fig. 1), which was resulted from the westward subduction of the Garze–Litang oceanic crust (Hu et al., 2010, Deng et al., 2014, Deng et al., 2015, …
Tectonic environments of South American porphyry copper magmatism
These results may be used as an exploration tool for determining likely locations where porphyry copper deposits have formed along the South American margin through time. 6.3 Identifying Nondeposits and Machine Learning Quandaries. Success using machine learning relies on the ability to characterize the data set of interest.
This AI Hunts for Hidden Hoards of Battery Metals
To realize a global transition to electric vehicles, we'll need to discover and mine an additional US $15 trillion worth of cobalt, copper, lithium, and nickel by midcentury. (We're currently ...
Advances in geological models and exploration methods
Alteration minerals, as the footprints of the hydrothermal mineralization process, are the focus of ore geology and exploration indicator research [12, [17] [18][19][20][21][22][23][24]. The ...
Machine learning coupled with mineral geochemistry …
The ore bodies dip 30°–60° towards 020°–030°. The ore bodies have lateral and vertical lengths of 100–150 and 30–90 m, respectively, with thicknesses of 0.5–8.0 m. The ores are mainly massive, banded, or stockwork. This type of mineralization occurs primarily in the Zhenzigou, Diannan, and Gaojiapuzi deposits (Fig. 3).
Exploration Targeting in Porphyry Cu Systems …
The most distal alteration facies—the propylitic zone—has been traditionally regarded as a largely isochemical alteration domain (e.g., Sillitoe, 2010) containing little information of use in exploration once …
(PDF) Machine Learning—A Review of Applications in
Fairbanks, AK 99775, USA; [email protected]. * Correspondence: [email protected]. Abstract: Mineral resource estimation involves the determination of the grade and tonnage of a min-. eral ...
Machine Learning-Based Mapping for Mineral Exploration
We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, …
Core Logging Interpretation and Resources Estimation for
The profile showed that the copper ore zone presented in hydrothermal breccia and tuff with a depth of less than 100 m. The orientation of the ore zone was expected from Southeast to Northwest, N ...
Application of ASTER Remote Sensing Data to …
Porphyry copper ore is a vital strategic mineral resource. It is often associated with significant hydrothermal alteration, which alters the original mineralogical properties of the rock. Extracting alteration …
Travelling through deep time to find copper for …
With Goldman Sachs predicting copper demand to grow up to 600% by 2030 and global supply becoming increasingly strained, it is clear we need to find new and large deposits of copper fast. Getting ...