Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications

(2022) Yuyang He et al.   APPLIED GEOCHEMISTRY

A geologically-constrained deep learning algorithm for recognizing geochemical anomalies

(2022) Chunjie Zhang et al.   COMPUTERS & GEOSCIENCES

A Geologically Constrained Variational Autoencoder for Mineral Prospectivity Mapping

(2022) Renguang Zuo et al.   Natural Resources Research

Framework components for data-centric dry laboratories in the minerals industry: A path to science-and-technology-led innovation

(2022) Yousef Ghorbani et al.   Extractive Industries and Society-An International Journal

Automated machine learning pipeline for geochemical analysis

(2022) Germán H. Alférez et al.   Earth Science Informatics

Quantifying Uncertainties Linked to the Diversity of Mathematical Frameworks in Knowledge-Driven Mineral Prospectivity Mapping

(2022) Mehrdad Daviran et al.   Natural Resources Research

Graph Deep Learning Model for Mapping Mineral Prospectivity

(2022) Renguang Zuo et al.   Mathematical Geosciences

Dry laboratories – Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry

(2022) Yousef Ghorbani et al.   MINERALS ENGINEERING

Prospectivity modelling of Canadian magmatic Ni (± Cu ± Co ± PGE) mineral systems

(2021) Christopher J.M. Lawley et al.   ORE GEOLOGY REVIEWS

Geochemically Constrained Prospectivity Mapping Aided by Unsupervised Cluster Analysis

(2021) Shuai Zhang et al.   Natural Resources Research

Regional geology and tectonic framework of the Southern Indian domain, Trans-Hudson Orogen, Manitoba

(2021) Tania Martins et al.   CANADIAN JOURNAL OF EARTH SCIENCES

A review of machine learning in processing remote sensing data for mineral exploration

(2021) Hojat Shirmard et al.   REMOTE SENSING OF ENVIRONMENT

Machine learning for data-driven discovery in solid Earth geoscience

(2019) Karianne J. Bergen et al.   SCIENCE

Deep learning and its application in geochemical mapping

(2019) Renguang Zuo et al.   EARTH-SCIENCE REVIEWS

Machine Learning for the Geosciences: Challenges and Opportunities

(2019) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

(2019) Tao Sun et al.   ORE GEOLOGY REVIEWS

Ensemble learning: A survey

(2018) Omer Sagi et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Geometallurgical Approach to the Element-to-Mineral Conversion for the Nabbaren Nepheline Syenite Deposit

(2018) Camilo Mena Silva et al.   Minerals

A machine learning approach to geochemical mapping

(2016) Charlie Kirkwood et al.   JOURNAL OF GEOCHEMICAL EXPLORATION

Mean Absolute Percentage Error for regression models

(2016) Arnaud de Myttenaere et al.   NEUROCOMPUTING

Machine learning in geosciences and remote sensing

(2016) David J. Lary et al.   Geoscience Frontiers

Data- and knowledge-driven mineral prospectivity maps for Canada's North

(2015) J.R. Harris et al.   ORE GEOLOGY REVIEWS

Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China

(2015) Renguang Zuo et al.   ORE GEOLOGY REVIEWS

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

(2015) V. Rodriguez-Galiano et al.   ORE GEOLOGY REVIEWS

A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: Applications for predictive geological mapping

(2013) E.C. Grunsky et al.   JOURNAL OF GEOCHEMICAL EXPLORATION

Resource depletion, peak minerals and the implications for sustainable resource management

(2011) T. Prior et al.   GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS

Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China)

(2011) Renguang Zuo   JOURNAL OF GEOCHEMICAL EXPLORATION