##tihuan2##The utilization of Random Forests yielded remarkable classification accuracy (73.08%) and Kappa classification values (65.73%) for identifying alteration minerals. Lin et al. (2022) integrated the Sparrow Search Algorithm (SSA) with two ML models, namely the RF and Gradient Boosting Decision Tree (GBDT) ensemble model …
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##tihuan2##The classification of minerals has an important place in many engineering fields and such as geology, mining, geophysics, environment and in many fields such as mineral exploration, gemology, prospecting, ore processing. ... Classification of Minerals Using Machine Learning Methods @article{Onal2020ClassificationOM, …
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##tihuan2##In this study, a machine learn-based study on geological and mineral energy and mineral energy classification was proposed, and a three-dimensional geological entity model was constructed. After the cube of the entity model, the three-dimensional quantitative prediction model of the study was determined through the …
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##tihuan2##In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. ... various light weight classification machine ...
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##tihuan2##A pioneering study integrates laser-induced breakdown spectroscopy (LIBS) with Raman spectroscopy (RS) and applies machine learning (ML) to achieve exceptional accuracy in mineral identification. The combined approach not only leverages the strengths of both techniques but also enhances classification precision, achieving up to 98.4% …
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##tihuan2##In order to precisely measure the mineral classification along the fault zone, we have employed the modern methods of statistical tools of DR such as LDA, PCA, ML tools such as (SVM), BPNN, RBFN along with C–N modeling (Konaté et al., 2015). C–N modelling is an advanced technique for spotting anomalous zones (Ullah et al., 2022).
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##tihuan2##Introduction. Modern mining, sorting, and mineral use have been challenged by the scarcity of mineral resources. At this stage, the development and application of vision-based ore sorting equipment have become one of the mainstreams to increase economic benefits, improve mineral grades, and reduce mining costs (Ali and Frimpong, …
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##tihuan2##Mineral resource classification relies on the expert assessment of a qualified person (QP) to determine which blocks of a 3D mineral resource model are classified as measured, indicated, or inferred. ... 3.2.2 Classification with Machine Learning. The same settings that were used in Case Study I are used to conduct the …
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##tihuan2##The most widely used method for mineral type classification from a rock thin section is done by the observation of optical properties of a mineral in a polarized …
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##tihuan2##This application aims to automatize and statistically evaluate mineral classification and mineral formula calculation within a single integrated open-access …
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##tihuan2##Purpose: The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machine learning approaches. Materials and methods: A total of 147 patients were included in this retrospective single-center study. There were 12 men and 135 women with a mean age …
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##tihuan2##Machine Learning (ML) has found several applications in spectroscopy, including recognizing minerals and estimating elemental composition. ... We firstly reviewed and tested several ML approaches to mineral classification from the existing literature, and identified a novel approach for using Deep Learning algorithms for mineral classification ...
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##tihuan2##According to a review of machine learning in analysis of mineral using remote sensing data by Shirmard et al., Advanced Learning Techniques such as CNN have proven to be effective in discriminating against targeted aspects of system recovery. Convolutional neural networks (CNN), random forest (RF), and support vector …
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##tihuan2##In addition, the classification algorithm used to process SEM data yields a category named "Unknown", in which particle with an ambiguous composition was not allocated with a mineral name. The classification fails when the chemical composition of a mineral exceed the specified tolerance in distance in the Euclidian hyperspace due to ...
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##tihuan2##To address this challenge, SRK has successfully implemented machine learning clustering algorithms to develop more comprehensive mineral resource classification schemes. Applying machine learning terminology, these algorithms can be applied in a semi-supervised and unsupervised way, with results always subject to review and editing, if …
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##tihuan2##Several different machine learning algorithms were evaluated in order to tackle the segmentation problem, beginning with the simplest unsupervised based classification with no training data required. Conclusions. The application of machine learning algorithms to mineral segmentation of 3D µCT image has been presented.
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##tihuan2##In mineral processing, wet classification techniques are generally preferred over dry technologies ... Cross-flow air classifiers, also known as winnowing machines, achieve a separation of particles from the difference in trajectories of coarse and fine particles. It has the advantage of rapidly producing multiple product-size fractions from a ...
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##tihuan2##Mineral Classification Using Machine Learning and Images of Microscopic Rock Thin Section. Authors: Henrique Pereira Borges, Marilton Sanchotene de Aguiar Authors Info & Claims. Advances in Soft Computing: 18th Mexican International Conference on Artificial Intelligence, MICAI 2019, Xalapa, Mexico, October 27 – November 2, 2019, …
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##tihuan2##Minerals are classified based on their chemical composition and crystalline structure. What is the chemical basis for mineral classification? The chemical basis for mineral classification is the elements that make up the mineral. Minerals are classified into groups based on their dominant chemical elements.
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##tihuan2##In this article, we have attempted to provide the main criteria and methods for developing a generalized machine learning and deep learning approach aimed at fast automatic classification of mineral …
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##tihuan2##Machine learning techniques are applied to improve mineral identification using whole-spectrum analysis. Careful application of preprocessing steps, similarity scoring functions, and classification a...
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##tihuan2##Minerals are the structures formed by the combination of one or more elements as a result of geological process. The discipline which examines all aspects of minerals is called mineralogy. In mineralogy science, the definition and classification of minerals are made by taking into account many properties such as physical and …
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##tihuan2##In this study, performances of five shallow machine classification algorithms and a deep learning algorithm were compared for the goal of pixel-level mineral classification of Scanning Electron Microscopy - Energy Dispersive X-Ray …
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##tihuan2##In this paper on mineral prospectivity mapping, a supervised classification method called Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. …
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##tihuan2##Thereafter, we sought to categorize the minerals along the YBFZ using Machine Learning (ML) technologies and concentration-number (C–N) modeling using multiple WFSD-1 and WFSD-2 wells. ... (Chen et al., 2020). In order to precisely measure the mineral classification along the fault zone, we have employed the modern methods …
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##tihuan2##Mineral classification and segmentation is time-consuming in geological image processing. The development of machine learning methods shows promise as a technique in replacing manual classification.
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##tihuan2##DOI: 10.1016/j.geoen.2023.212077 Corpus ID: 259765223; Knowledge-based machine learning for mineral classification in a complex tectonic regime of Yingxiu-Beichuan fault zone, Sichuan basin
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##tihuan2##Mineral Classification Using Machine Learning and Images of Microscopic Rock Thin Section. Pages 63–76. Previous Chapter Next Chapter. Abstract. The most widely used method for mineral type classification from a rock thin section is done by the observation of optical properties of a mineral in a polarized microscope rotation stage. …
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##tihuan2##Semantic Scholar extracted view of "A review on hyperspectral imagery application for lithological mapping and mineral prospecting: Machine learning techniques and future prospects" by Soufiane Hajaj et al. ... Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification:a …
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