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Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Constructing a high-performance self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies for gold exploration targeting
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2023.105418openalex-snapshot
Self-paced ensemble for constructing an efficient robust high-performance classification model for detecting mineralization anomalies from geochemical exploration data
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2023.105706openalex-snapshot
A high-performance voting-based ensemble model of graph convolutional extreme learning machines for identifying geochemical anomalies related to mineralization
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2024.106263openalex-snapshot
Combining categorical boosting and Shapley additive explanations for building an interpretable ensemble classifier for identifying mineralization-related geochemical anomalies
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2025.106627openalex-snapshot
Hyperparameter optimization in unsupervised anomaly detection for mineral prospectivity mapping
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2025.106787openalex-snapshot
A robust soft voting ensemble of the isolation forest model, extended isolation forest model and generalized isolation forest model for multivariate geochemical anomaly recognition
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2026.107147openalex-snapshot
Tracing copper mineralisation anomalies using multi-element geochemistry and machine learning in the Okiep Copper District, South Africa
inferred← citeshttps://doi.org/10.1016/j.rser.2025.116685openalex-snapshot
A comprehensive review of green hydrogen production via electrolysis and thermolysis, and the prediction of potential natural hydrogen (aka gold hydrogen) presence using machine learning
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.03.295openalex-snapshot
Bivariate probability plots: A method for delineating different populations in soil geochemical data
inferred← citeshttps://doi.org/10.1016/j.watres.2025.124823openalex-snapshot
Toward generalizable machine learning models for dichloroacetonitrile formation: Interpretable insights and a framework for model reliability
inferred← citeshttps://doi.org/10.1029/2024jh000476openalex-snapshot
Examining the Quantification Capability of Automated Mineralogy System: A Machine Learning Approach
inferred← citeshttps://doi.org/10.1063/5.0245419openalex-snapshot
Predicting the depth of rock cutting by abrasive water jet using support vector machine optimized with whale optimization algorithm
inferred← citeshttps://doi.org/10.1109/access.2022.3149625openalex-snapshot
Estimation of Filtration Properties of Host Rocks in Sandstone-Type Uranium Deposits Using Machine Learning Methods
inferred← citeshttps://doi.org/10.1109/bigdata50022.2020.9378192openalex-snapshot
An Autocorrelation-based LSTM-Autoencoder for Anomaly Detection on Time-Series Data
inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11228151openalex-snapshot
Research on the method of expanding the metallogenic data based on generative adversarial networks
inferred← citeshttps://doi.org/10.1134/s0016702920040084openalex-snapshot
Comparative Analysis of Geochemical Data Processing Methods for Allocation of Anomalies and Background
inferred← citeshttps://doi.org/10.1144/geochem2017-901openalex-snapshot
Introduction to the thematic issue: analysis of exploration geochemical data for mapping of anomalies
inferred← citeshttps://doi.org/10.1144/geochem2021-096openalex-snapshot
Identification of stream sediment geochemical anomalies in lithologically complex regions: case study of Cu mineralization in Hunan province, SE China
inferred← citeshttps://doi.org/10.1144/geochem2022-016openalex-snapshot
Combining neighbourhood component analysis with dictionary learning algorithms to improve the performance of dictionary learning models for geochemical anomaly detection
inferred← citeshttps://doi.org/10.1144/geochem2023-070openalex-snapshot
Geochemical anomaly recognition using Shapley values and cell-wise outlier detection: a case study in the Yuanbo Nang District, Gansu Province, China
inferred← citeshttps://doi.org/10.1144/geochem2024-009openalex-snapshot
Effectiveness of LOF, iForest and OCSVM in detecting anomalies in stream sediment geochemical data
inferred← citeshttps://doi.org/10.1144/geochem2025-050openalex-snapshot
Accumulation coefficient for exploration feature selection: a tool for ranking geochemical indicators and use in prospectivity mapping
inferred← citeshttps://doi.org/10.1155/2022/9443955openalex-snapshot
Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs
inferred← citeshttps://doi.org/10.3390/app11125541openalex-snapshot
From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review
inferred← citeshttps://doi.org/10.3390/app121910029openalex-snapshot
Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China
inferred← citeshttps://doi.org/10.3390/app122211433openalex-snapshot
Prediction of Prospecting Target Based on ResNet Convolutional Neural Network
inferred← citeshttps://doi.org/10.3390/app15084082openalex-snapshot
Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China
inferred← citeshttps://doi.org/10.3390/app16136658openalex-snapshot
Assessment of Multiple Machine Learning Models for Critical Mineral Exploration Data Quality in the Brazilian Shield
inferred← citeshttps://doi.org/10.3390/jcp2020012openalex-snapshot
Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
inferred← citeshttps://doi.org/10.3390/math10152552openalex-snapshot
Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges
inferred← citeshttps://doi.org/10.3390/math11224687openalex-snapshot
Determination of Reservoir Oxidation Zone Formation in Uranium Wells Using Ensemble Machine Learning Methods
inferred← citeshttps://doi.org/10.3390/min14050500openalex-snapshot
Interpretable Machine Learning for Geochemical Anomaly Delineation in the Yuanbo Nang District, Gansu Province, China
inferred← citeshttps://doi.org/10.3390/min14090957openalex-snapshot
Geological Controlling Factors on Mississippi Valley-Type Pb-Zn Mineralization in Western Semnan, Iran
inferred← citeshttps://doi.org/10.3390/min14101021openalex-snapshot
A Review of Mineral Prospectivity Mapping Using Deep Learning
inferred← citeshttps://doi.org/10.3390/min15010071openalex-snapshot
DCGAN-Based Feature Augmentation: A Novel Approach for Efficient Mineralization Prediction Through Data Generation
inferred← citeshttps://doi.org/10.3390/min15040411openalex-snapshot
Unsupervised Anomaly Detection for Mineral Prospectivity Mapping Using Isolation Forest and Extended Isolation Forest Algorithms
inferred← citeshttps://doi.org/10.3390/min16030330openalex-snapshot
GeoCLA: An Integrated CNN-BiLSTM-Attention Framework for Geochemical Anomaly Detection in the Hatu Region, Xinjiang
inferred← citeshttps://doi.org/10.3390/min16070700openalex-snapshot
Beyond Traditional Methods: Machine Learning for Geochemical Baselines and Anomaly Detection
inferred← citeshttps://doi.org/10.3390/min9050317openalex-snapshot
A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping
inferred← citeshttps://doi.org/10.47749/t/unicamp.2022.1259854openalex-snapshot
Evaluation of machine learning methods for prospectivity modeling and vectoring of IOCG mineralization
inferred← citeshttps://doi.org/10.70675/1d621c6bz2e10z411bzad3cz57634e2b72deopenalex-snapshot
Detection of atypical events for security in critical infrastructure
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
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