Receiver operating characteristics (ROC) as validation tool for prospectivity models — A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland

No abstract is available for this paper.

• Publication year

  2015

• Venue

  Ore Geology Reviews

• Publication date

  2015-12-01

• Fields of study

  Geology

• Identifiers
  DOI 10.1016/J.OREGEOREV.2014.09.007
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Curvature Attribute from Surface-Restoration as Predictor Variable in Kupferschiefer Copper Potentials

  2015cited by this paper
• The application of rapid potential field methods for the targeting of IOCG mineralisation based on physical property data, Great Bear magmatic zone, Canada

  2013cited by this paper
• Regional prospectivity analysis for hydrothermal-remobilised nickel mineral systems in western Victoria, Australia

  2013cited by this paper
• Mineral potential mapping in frontier regions: A Mongolian case study

  2013cited by this paper
• Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling

  2013cited by this paper
• Tectonic–geochemical exploration modeling for characterizing geo-anomalies in southeastern Yunnan district, China

  2012cited by this paper
• Reconnaissance-Scale Prospectivity Analysis for Gold Mineralisation in the Southern Uplands-Down-Longford Terrane, Northern Ireland

  2012cited by this paper
• Geochemical Anomaly and Mineral Prospectivity Mapping in Gis

  2012cited by this paper
• Mineral potential mapping for orogenic gold deposits in the Rio Maria granite greenstone terrane, southeastern Para State, Brazil

  2012cited by this paper
• Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: Mineral system analysis, targeting model and prospectivity analysis

  2012cited by this paper
• Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration

  2012cited by this paper
• Application of geochemical zonality coefficients in mineral prospectivity mapping

  2011cited by this paper
• A multifractal simulation model for the distribution of VMS deposits in the Spanish segment of the Iberian Pyrite Belt

  2011cited by this paper
• Regional Exploration Targeting Model for Gangdese Porphyry Copper Deposits

  2011cited by this paper
• Gold resources potential assessment in eastern Kunlun Mountains of China combining weights-of-evidence model with GIS spatial analysis technique

  2010cited by this paper
• Developing models using GIS to assess geological and economic risk: An example from VMS copper gold mineral exploration in Oman

  2010cited by this paper
• Prospectivity analysis of the Plutonic Marymia Greenstone Belt, Western Australia

  2010cited by this paper
• Magmatic nickel sulfide mineralization in Zimbabwe: Review of deposits and development of exploration criteria for prospectivity analysis

  2010cited by this paper
• Hydrothermal Ni prospectivity analysis of Tasmania, Australia

  2010cited by this paper
• Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia

  2010cited by this paper
• Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

  2009cited by this paper
• Mapping of prospectivity and estimation of number of undiscovered prospects for lode gold, southwestern Ashanti Belt, Ghana

  2009cited by this paper
• Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity

  2009cited by this paper
• Gold potential of the Dalradian rocks of north-west Northern Ireland: Prospectivity analysis using Tellus data

  2009cited by this paper
• Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield

  2008cited by this paper
• Predicting landslides for risk analysis — Spatial models tested by a cross-validation technique

  2008cited by this paper
• On Blind Tests and Spatial Prediction Models

  2008cited by this paper
• Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland

  2008cited by this paper
• Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping

  2008cited by this paper
• Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper – gold deposits in the northern Fennoscandian Shield, Finland

  2008cited by this paper
• Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield, Finland

  2007cited by this paper
• prospectivity AnAlysis of golD using regionAl geophysicAl AnD geochemicAl DAt A from the centrAl lAplAnD greenstone belt , finlAnD

  2007cited by this paper
• Probabilistic Prediction Models for Aggregate Quarry Siting

  2007cited by this paper
• Gold prospectivity maps of the Red Lake greenstone belt: application of GIS technology

  2006cited by this paper
• An introduction to ROC analysis

  2006cited by this paper
• Precambrian geology of Finland : key to the evolution of the fennoscandian shield

  2005cited by this paper
• Measuring the Performance of Mineral-Potential Maps

  2005cited by this paper
• Magmatic Sulfide Deposits: Geology, Geochemistry and Exploration

  2004cited by this paper
• An Artificial Neural Network Method for Mineral Prospectivity Mapping: A Comparison with Fuzzy Logic and Bayesian Probability Methods

  2003cited by this paper
• Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

  2003cited by this paper
• Receiver operating characteristic curves and their use in radiology.

  2003cited by this paper
• Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

  2003cited by this paper
• Three in One.

  2001cited by this paper
• Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt, Ontario, Canada

  2001cited by this paper
• Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines

  2001cited by this paper
• Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential, Baguio District, Philippines

  2001cited by this paper
• Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation

  2000cited by this paper
• Artificial neural networks: A new method for mineral prospectivity mapping

  2000cited by this paper
• Vectorial fuzzy logic: A novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane, Western Australia

  2000cited by this paper
• Gravity investigations of the geological survey of finland 1995-1997

  1998cited by this paper
• Interpretations of the gravity anomaly map of Finland

  1997cited by this paper
• Gold, Palladium and Tellurium in South African, Chinese and Japanese Geological Reference Samples

  1990cited by this paper

• National-scale mineral potential assessment of Cenozoic heavy mineral sand rare earth element systems in Australia

  2026cites this paper
• Spatial-Aware Parametric UMAP Network: An Interpretable Deep Learning Analytical Framework for Mineral Prospectivity Mapping

  2026cites this paper
• Identification of Geochemical Anomalies Using a Deep Semi-supervised Anomaly Detection Model

  2026cites this paper
• Prospectivity Mapping of Targets for Li-Bearing Pegmatites and Granites in Västernorrland, Sweden, with Fuzzy Logic and Random Forest Modeling

  2026cites this paper
• Improving Geochemical Anomaly Recognition Associated with Mineralization via Geological Knowledge Interacting Reinforcement Learning

  2025cites this paper
• Recent advances and future research directions in deep learning as applied to geochemical mapping

  2025cites this paper
• Deep learning and fractal-wavelet techniques for magnetite-apatite exploration in Tarom Iran

  2025cites this paper
• Research dynamics and frontier trends of orogenic gold deposits: a knowledge map based on bibliometrics

  2025cites this paper
• Risk Assessment of Geological Hazards in Dawukou, Shizuishan City Based on the Information Value Model

  2025cites this paper
• Mineral prospectivity analysis is unstable to changes in pixel size

  2025cites this paper
• ROC Curves for Spatial Point Patterns and Presence-Absence Data

  2025influential citation
• Assessing forest fire vulnerability using artificial neural networks in Almora district, Uttarakhand, India

  2025influential citation
• Integration of WorldView-3 Remote Sensing and Classification Techniques for Mapping Iron Oxide Mineralization in MVT Pb-Zn Deposits, Jbel Bou Dahar, Morocco

  2025influential citation
• Enhancing porphyry copper prospectivity mapping: A deep autoencoder-based approach to identify non-deposit points in varzaghan region, NW Iran

  2025cites this paper
• Three-Dimensional Prospectivity Modeling of Jinshan Ag-Au Deposit, Southern China by Weights-of-Evidence

  2025cites this paper
• Exploration information system: mineral systems anatomy linked to computational techniques for mineral exploration targeting

  2025cites this paper
• Machine learning-based mineral prospectivity mapping of epithermal gold mineralization in Northern new brunswick: a comparative study of random forest, support vector machine, and XGboost classifiers

  2025cites this paper
• Controls, Expressions, and Discovery Potential of Gold Mineralization in the Central-Eastern Yilgarn Craton, Western Australia: New Insights from an Integrated Targeting Study

  2025cites this paper
• A Modulated Mineral Prospectivity Mapping of Orogenic Gold Mineralization, Pietersburg Greenstone Belt, South Africa: Exploration Targeting from a Mineral Systems Approach

  2025influential citation
• An interpretable causal variational autoencoder for geochemical anomalies recognition by regarding ore-controlling factors

  2025cites this paper
• A generative neural network approach to uncertainty and risk-return analysis in mineral prospectivity modelling

  2025cites this paper
• Optimizing Aggregate Resource Suitability: A GIS-Based Approach for Sustainable Development

  2025cites this paper
• Positive–Unlabeled Learning-Based Hybrid Models and Interpretability for Groundwater Potential Mapping in Karst Areas

  2025cites this paper
• Self-Supervised Graph Contrastive Learning for Mineral Prospectivity Mapping

  2025cites this paper
• Interpretable machine learning for mineral prospectivity mapping in the Qulong–Jiama district, Tibet, China

  2025cites this paper
• Class Label Representativeness in Machine Learning-Based Mineral Prospectivity Mapping

  2025cites this paper
• Hyperparameter optimization in unsupervised anomaly detection for mineral prospectivity mapping

  2025cites this paper
• Multisource data fusion for enhanced gold mineral prospectivity mapping in Yagba West, Kogi State: a machine learning approach

  2025cites this paper
• Transforming LCT Pegmatite Targeting Models into AI-Powered Predictive Maps of Lithium Potential for Western Australia and Ontario: Approach, Results and Implications

  2025cites this paper
• Geological-knowledge-guided graph self-supervised pretraining framework for identifying mineralization-related geochemical anomalies

  2025cites this paper
• Rotation-based outlier detection for geochemical anomaly identification in stream sediment multivariate data

  2025cites this paper
• Mineral Prospectivity Mapping in Xiahe-Hezuo Area Based on Wasserstein Generative Adversarial Network with Gradient Penalty

  2025cites this paper
• Tungsten prospectivity mapping using multi-source geo-information and deep forest algorithm

  2025cites this paper
• BWM-MARCOS: A new hybrid MCDM approach for mineral potential modelling

  2025cites this paper
• Implementation of machine learning predictive models for targeting gold prospectivity mapping in part of the Ilesha schist belt, southwestern Nigeria

  2025cites this paper
• Coupling Graph Attention Networks with Causal Discovery for Geochemical Anomaly Recognition

  2025cites this paper
• Recursive Annotation for Negative Labeling in Data-Driven Mineral Prospectivity Mapping

  2025influential citation
• Mapping Cu–Au mineral potential (IOCG and Cu–Au polymetallic) in the Northern copper belt, Carajás Mineral Province: A data-driven, mineral systems-based approach

  2025cites this paper
• Geologically Constrained Convolutional Neural Network for Mineral Prospectivity Mapping

  2024cites this paper
• A balanced mineral prospectivity model of Canadian magmatic Ni (± Cu ± Co ± PGE) sulphide mineral systems using conditional variational autoencoders

  2024cites this paper
• A novel approach for integrating spatial data in gold prospectivity mapping using a modified TODIM method, a case study: Basiran-Mokhtaran - Eastern Iran

  2024cites this paper
• Mapping of Gold Prospectivity in the Qingchengzi Pb–Zn–Ag–Au Polymetallic District, China, with Ensemble Learning Algorithms

  2024influential citation
• Translating mineral systems criteria into a prospectivity model for IOCG deposits in the Kolari region, Finland

  2024cites this paper
• Addressing imbalanced data for machine learning based mineral prospectivity mapping

  2024cites this paper
• Evaluation of Deep Isolation Forest (DIF) Algorithm for Mineral Prospectivity Mapping of Polymetallic Deposits

  2024cites this paper
• Porphyry-type mineral prospectivity mapping with imbalanced data via prior geological transfer learning

  2024cites this paper
• Mineral prospectivity mapping susceptibility evaluation based on interpretable ensemble learning

  2024cites this paper
• Enhancing training performance of convolutional neural network algorithm through an autoencoder-based unsupervised labeling framework for mineral exploration targeting

  2024cites this paper
• Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification

  2024cites this paper
• Geo-Hgan: Unsupervised anomaly detection in geochemical data via latent space learning

  2024cites this paper
• Explainable artificial intelligence models for mineral prospectivity mapping

  2024cites this paper
• Exploring multi-pollution variability in the urban environment: geospatial AI-driven modeling of air and noise

  2024cites this paper
• Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model

  2024cites this paper
• 可解释性矿产预测人工智能模型

  2024cites this paper
• A Review of Integration Techniques of Multi-Geoscience Data-Sets in Mineral Prospectivity Mapping

  2024cites this paper
• Predictive Modeling of Canadian Carbonatite-Hosted REE +/− Nb Deposits

  2024cites this paper
• A SMOTified-GAN-augmented bagging ensemble model of extreme learning machines for detecting geochemical anomalies associated with mineralization

  2024cites this paper
• Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms

  2024cites this paper
• Wildfire Vulnerability Assessment and Mapping Using Remote Sensing, GIS and Weighted Overlay Method in the Eastern Aures in Khenchela, Algeria

  2024cites this paper
• Dual-Branch Convolutional Neural Network and Its Post Hoc Interpretability for Mapping Mineral Prospectivity

  2024cites this paper
• Mapping bauxite-associated alterations in the Boke region (NW part of Republic of Guinea), using the airborne gamma-ray spectrometry data

  2024cites this paper
• Three-dimensional prospectivity modeling based on fuzzy logic and geometric average: a case study from Sansong district, Yongyu apatite deposit, DPR Korea

  2024cites this paper
• High-performance imbalanced learning ensembles of decision trees for detecting mineralization anomalies from geochemical exploration data

  2024cites this paper
• Recognition of REEs anomalies using an image Fusion fractal-wavelet model in Tarom metallogenic zone, NW Iran

  2024cites this paper
• Porphyry and skarn Cu prospectivity of Sumatra with GIS spatial modelling techniques

  2024cites this paper
• Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS)

  2024cites this paper
• Quantifying uncertainty and improving prospectivity mapping in mineral belts using transfer learning and Random Forest: A case study of copper mineralization in the Superior Craton Province, Quebec, Canada

  2024cites this paper
• Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research

  2024cites this paper
• GIS-based mineral prospectivity mapping using machine learning methods: a case study from Zhuonuo ore district, Tibet

  2023cites this paper
• ArcMPM: An ArcEngine-Based Software for Mineral Prospectivity Mapping via Artificial Intelligence Algorithms

  2023cites this paper
• Combining numerical modeling and machine learning to predict mineral prospectivity: A case study from the Fankou Pb–Zn deposit, southern China

  2023cites this paper
• A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran

  2023cites this paper
• 3D Au Targeting using Machine Learning with Different Sample Combination and Return-Risk Analysis in the Sanshandao-Cangshang District, Shandong Province, China

  2023cites this paper
• 3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data

  2023cites this paper
• A Framework for Data-Driven Mineral Prospectivity Mapping with Interpretable Machine Learning and Modulated Predictive Modeling

  2023cites this paper
• Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting

  2023cites this paper
• An integrated approach for rapid exploration of carbonatites and related mineral resources

  2023cites this paper
• Mineral Prospectivity Mapping for Orogenic Gold Mineralization in the Rainy River Area, Wabigoon Subprovince

  2023cites this paper
• Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran

  2023cites this paper
• One-dimensional Convolutional Neural Network for Mapping Mineral Prospectivity: A Case Study in Changba Ore Concentration Area, Gansu Province

  2023cites this paper
• Cobalt Prospectivity Using a Conceptual Fuzzy Logic Overlay Method Enhanced with the Mineral Systems Approach

  2023cites this paper
• Mineral prospectivity mapping based on Support vector machine and Random Forest algorithm-A case study from Ashele copper-zinc deposit, Xinjiang, NW China

  2023cites this paper
• Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data

  2023cites this paper
• The graph attention network and its post-hoc explanation for recognizing mineralization-related geochemical anomalies

  2023cites this paper
• Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling

  2023influential citation
• Prospectivity Mapping of Tungsten Mineralization in Southern Jiangxi Province Using Few-Shot Learning

  2023cites this paper
• A study of faults in the Superior province of Ontario and Quebec using the random forest machine learning algorithm: spatial relationship to gold mines

  2023cites this paper
• Best worst method-based mineral prospectivity modeling over the Central part of the Southern Kibi-Winneba Belt of Ghana

  2023cites this paper
• Detection and mapping of vegetation stress using AVIRIS-NG hyperspectral imagery in coal mining sites

  2023cites this paper
• Mineral Prospectivity Mapping Using Machine Learning Techniques for Gold Exploration in the Larder Lake Area, Ontario, Canada

  2023cites this paper
• Bagging-based Positive–Unlabeled Data Learning Algorithm with Base Learners Random Forest and XGBoost for 3D Exploration Targeting in the Kalatongke District, Xinjiang, China

  2023cites this paper
• Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet

  2023cites this paper
• Strategies for Targeting in Undercover Terrains: Modeling Multi-Source Data in Apuí Region, SW Amazon Craton, Brazil

  2023cites this paper
• Identification of geochemical anomalies related to mineralization: A case study from porphyry copper deposits in the Qulong-Jiama mining district of Tibet, China

  2022cites this paper
• Information value-based geochemical anomaly modeling: A statistical index to generate enhanced geochemical signatures for mineral exploration targeting

  2022cites this paper
• Recognition and incorporation of mineralization-efficient fault systems to produce a strengthened anisotropic geochemical singularity

  2022cites this paper
• Cu–Au exploration target generation in the eastern Carajás Mineral Province using random forest and multi-class index overlay mapping

  2022cites this paper
• Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland

  2022cites this paper
• Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 1: Application of knowledge-driven- and machine learning-based-hybrid- expert systems for exploration targeting and addressing model-based uncertainties

  2022cites this paper
• Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites.

  2022cites this paper