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MIT associate professor of metallurgy Antoine Allanore has received a $1.9 million grant from the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) to run larger scale …
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@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}, …
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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 …
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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 …
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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 ...
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Applied across the industry, improved coarse particle flotation can result in an additional 0.5 million to 1.5 million metric tons of annual copper production by 2032. If applied across all metals found in …
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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 ...
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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, …
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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 ...
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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 …
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Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco) ... Ore Geology Reviews 143 ...
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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 ...
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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).
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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, …
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The combined use of remote sensing data and machine learning algorithms have proven to facilitate and improve mineral exploration. Machine learning methods draw a growing interest in the area of remote sensing data analysis as a solution to the problems of geological or mineral exploration (Bachri et al., 2019).
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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.
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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 ...
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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 …
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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 ...
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In this paper, the applicability of various machine learning techniques like artificial neural network (ANN), extreme learning machine (ELM), gradient boosted decision trees (GBDT), random forests (RF), support vector regression (SVR) have been discussed for ore grade estimation of different mineral deposits like iron, gold and copper. This ...
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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.
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The combined use of remote sensing data and machine learning algorithms have proven to facilitate and improve mineral exploration. Machine learning methods draw a growing interest in the area of remote sensing data analysis as a solution to the problems of geological or min-eral exploration (Bachri et al., 2019).
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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 ...
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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 …
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The chemical compositions of zircons have been used as magma fertility indicators for porphyry copper mineralization potential. Here, we trained and tested two machine learning (ML) models: support vector machines and random forest to classify the metallogenic fertility (fertile vs. unfertile) based on igneous zircon chemistry. Both models …
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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 …
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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 ...
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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 information from remote sensing data is crucial for porphyry copper exploration. However, the current method of extracting …
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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 …
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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.
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The Copper River & Northwestern Railway, nicknamed " Can't Run and Never Will," was built between 1907 and 1911. At 196 miles long, with 30 miles of bridges and trestles, it took 6,000 men, and $23 million to build. Within days of completion, the frst trainload of ore, worth $250,000, rolled down to Cordova.
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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. ...
DetailsStream sediments have been used in mineral exploration for millennia, as one of the older mining and prospecting methods, advancing from gold or diamond panning to modern geochemical approaches (Ottesen and Theobald 1994; Stendal and Theobald 1994).Stream sediments have historically provided the most cost-effective sampling …
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China's research into metallogenic models began relatively late. In 1966, the descriptive model of the black tungsten ore-quartz vein "Five-story Pagoda" was first constructed, marking the initiation of China's research into metallogenic models (Wang, 2010).In 1979, Professor Cheng and Chen proposed a new theory in metallogenic series …
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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 …
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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 …
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In this study, machine learning methods such as neural networks, random forests, and Gaussian processes are applied to the estimation of copper grade in a mineral deposit. The performance of these methods is compared to geostatistical techniques, such as ordinary kriging and indicator kriging. To ensure that these comparisons are realistic …
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