Three-dimensional modelling of alteration zones based on geochemical exploration data: An interpretable machine-learning approach via generalized additive models Chen, Jin Mao, Xiancheng
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· Extreme learning Machine (ELM) is a novel supervised machine learning algorithm, which has the advantages of fast-learning speed, good generalization, high classification performance, and can avoid problems such as local minimum, unreasonable learning rate, excessive number of iterations and overfitting. However, its classification
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:Machine LearningGeochemical Exploration DataPublish Year:2021Effectiveness of LOF, iForest, and OCSVM in detecting anomalies in stream sediment geochemical data. New methodological approach for deep penetrating geochemistry
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Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation Chetan L. Nathwani 1,2 · Jamie J. Wilkinson 1,2
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· Abstract. Identifying multivariate anomalies from geochemical exploration data in a complex geological setting is very challenging because the complex Yongliang Chen, Wei Wu; Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data.
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:Geochemical Exploration DataGeochemistry in Mineral ExplorationDavid R. Cohen · Hence, we address this major gap by demonstrating a dual-use integration of machine learning in a typical geochemical exploration pipeline to both
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· Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation October 2022 Mineralium Deposita 57(65)
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:Journal of Geochemical ExplorationGeochemistry in Mineral Exploration · Exploration geochemistry involves the assessment of chemical and mineralogical data obtained from a variety of sampling media to locate new mineral
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· A workflow for the intelligent geochemical data processing was proposed. • GIS and machine learning algorithms are two key components for the workflow. Geochemical exploration has provided significant clues for mineral exploration and has helped discover many mineral deposits..
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· Pertti Sarala, Solveig Pospiech, Maarit Middleton, and. José-Paulo Pinheiro. GEEAArticle27 May 2024. New methodological approach for deep penetrating geochemistry and environmental studies, Part 2: field determination of Co (II)/Ni (II) and Pb (II)/Zn (II) of on-site soil extractions by electrochemical stripping techniques.
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· Big data mining with machine learning and multivariate statistical analyses indicates that: (1) Sphalerite from MVT, VMS, SEDEX, epithermal, porphyry, and skarn deposits is enriched in Ge, Mn, Fe, Sn, As, and Co, respectively, and that from MVT, VMS, and SEDEX deposits is also characterized by depleted In Mn, Sn, and As, respectively. (2)
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Explor. 227 (2021) 106793] Juan José Egozcue, Vera Pawlowsky-Glahn, Antonella Buccianti. Article 106860. View PDF. Previous vol/issue. Next vol/issue. Read the latest articles of Journal of Geochemical Exploration at ScienceDirect.com, Elsevier’s leading platform of peer-reviewed scholarly literature.
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· In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples
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· Traditionally, methods and techniques for analysis of geochemical data for mapping of anomalies to support mineral exploration broadly fall into two categories of statistical analysis (Levinson 1974; Rose et al. 1979; Howarth 1983): (1) univariate analysis; and (2) multivariate analysis.
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:Machine LearningGeochemical Exploration DataPublish Year:2021 · Geochemical exploration, or exploration geochemistry, is a spatial sampling and analysis methodology used extensively in the search for mineral resources
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The processing methods of geochemical exploration data: past, present, and future. Renguang Zuo a,*, Jian Wangb, Yihui Xiong a, Ziye Wang a. State Key Laboratory of Geological Processes and Mineral Resources, China University of
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· Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics
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:Machine LearningGeochemical Exploration Data · As a successor, this current thematic collection introduces some new progress and advancements in geochemical data analysis, e.g. nonlinear,
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DOI: 10.1016/J.APGEOCHEM.2020.104781 Corpus ID: 224983700 Three-dimensional modelling of alteration zones based on geochemical exploration data: An interpretable machine-learning approach via generalized additive models @article
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· A workflow for interpretation and visualization of geochemical exploration data (Fig. 1) has two key components: GIS and machine learning algorithms, which can be used for managing geochemical exploration
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· One-class support vector machine (OCSVM) can give useful results in outlier detection in high-dimension or without any assumptions on the distribution of data. Thus, we applied the OCSVM model to identify multivariate geochemical anomalies from stream sediment survey data of the Lalingzaohuo district, an area with complex
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· As a successor, this current thematic collection introduces some new progress and advancements in geochemical data analysis, e.g. nonlinear, fractal/multifractal, multi-statistical, machine learning methods and their applications in mineral exploration.
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· Introduction. Exploration geochemistry involves the assessment of chemical and mineralogical data obtained from a variety of sampling media to locate new mineral deposits and energy resources. In the case of mineral deposits this includes characterizing and modeling geochemical patterns and processes within the primary
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Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation Chetan L. Nathwani1,2 · Jamie J. Wilkinson1,2 · George Fry3 · Robin N. Armstrong1 · Daniel J. Smith4 · 3
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· Definition. Geochemical exploration, or exploration geochemistry, is a spatial sampling and analysis methodology used extensively in the search for mineral resources and routinely for petroleum. The approach is based on the natural dispersion of elements, minerals, and compounds from exposed or concealed mineral or petroleum
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· In the paper, we presented an interpretable machine-learning approach to the 3D modelling of alteration zones based on geochemical exploration data. Using the proposed approach, a geological model of phyllic alteration zones in a rarely-drilled area around the Dayingezhuang deposit was constructed.
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A current mineral exploration focus is the development of tools to identify magmatic districts predisposed to host porphyry copper deposits. In this paper, we train and test four, common, supervised machine learning algorithms: logistic regression, support vector machines, artificial neural networks (ANN) and Random Forest to classify metallogenic ‘fertility’ in
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In this paper, we propose an interpretable machine-learning method to infer 3D geological models of alteration zones based on geochemical data from the Dayingezhuang area, China. To expose the information hidden in geochemical data that is indirectly and weakly related to alteration zones, a generalized additive model (GAM) is used.
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DOI: 10.1016/j.aiig.2022.10.003 Corpus ID: 253378827 Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns @article{Zhang2022AdvancedGE
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. Journal of Geochemical Exploration is mostly dedicated to publication of original studies in exploration and environmental geochemistry and related topics. Contributions considered of prevalent interest for the journal include researches based on the application of innovative methods to: define the genesis and the evolution of
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