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Classification of mineral deposits into types using mineralogy with a probabilistic neural network

https://doi.org/10.1007/bf02816922
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Induction of Decision Trees
inferredcites →https://doi.org/10.1007/bf02068587openalex-snapshot
Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan
inferredcites →https://doi.org/10.1016/0148-9062(96)81894-8openalex-snapshot
Development of grade and tonnage models for different deposit types
inferredcites →https://doi.org/10.1016/0893-6080(90)90049-qopenalex-snapshot
Probabilistic neural networks
inferredcites →https://doi.org/10.1016/c2009-0-27872-xopenalex-snapshot
Introduction to Statistical Pattern Recognition
inferredcites →https://doi.org/10.1214/aoms/1177704472openalex-snapshot
On Estimation of a Probability Density Function and Mode
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Geologic and grade-tonnage information on Tertiary epithermal precious- and base-metal vein districts associated with volcanic rocks
inferredcites →https://doi.org/10.3133/ofr8389openalex-snapshot
Geologic and grade-tonnage information on volcanic-hosted copper-zinc-lead massive sulfide deposits

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A
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A Complex Systems Model Approach to Quantified Mineral Resource Appraisal
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Correspondence Analysis for Mineral Commodity Research: An Example Workflow for Mineralized Calderas, Southwest United States
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Dual-Branch Transformer Model Considering Processes and Spatial Distribution of Mineralization for 3D Mineral Prospectivity Mapping
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A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran
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Investigation of general regression neural network architecture for grade estimation of an Indian iron ore deposit
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Supervised geochemical anomaly detection by pattern recognition
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Incorporation of geological constraints and semivariogram scaling law into geostatistical modeling of metal contents in hydrothermal deposits for improved accuracy
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Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration
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Application of a hybrid method combining multilevel fuzzy comprehensive evaluation with asymmetric fuzzy relation analysis to mapping prospectivity
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Progress in integrated quantitative mineral resource assessments
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Data- and knowledge-driven mineral prospectivity maps for Canada's North
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GIS-based mineral prospectivity mapping using machine learning methods: A case study from Zhuonuo ore district, Tibet
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Predicting mineralogy by integrating core and well log data using a deep neural network
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Data-driven fuzzy analysis in quantitative mineral resource assessment
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Some Simple Guides to Finding Useful Information in Exploration Geochemical Data
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Quantitative Analysis of Mineral Resources for Strategic Planning: Implications for Australian Geological Surveys
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Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District, Northern Japan, for Exploring Kuroko-Type Deposits
inferred← citeshttps://doi.org/10.1023/a:1021606417010openalex-snapshot
A Comparison of the Weights-of-Evidence Method and Probabilistic Neural Networks
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Computer Expert System for Epithermal Gold-Silver Deposit Prognostication (Okhotsk–Chuckchee Volcanic Belt, Northeast Russia)
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Use of a Probabilistic Neural Network to Reduce Costs of Selecting Construction Rock
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Typing Mineral Deposits Using Their Grades and Tonnages in an Artificial Neural Network
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Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India
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Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping
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Artificial neural networks: A new method for mineral prospectivity mapping
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Comparing the Performance of Different Neural Networks Architectures for the Prediction of Mineral Prospectivity
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Use of polynomial neural network for a mineral prospectivity analysis in a GIS environment
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Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction
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Dual Component Estimation; a probabilistic approach to estimate undiscovered oil and gas in an underexplored basin, Taranaki Basin, New Zealand
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Artificial intelligence strategy for the U.S. Geological Survey
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Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China
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Utilizing Multifractal and Compositional Data Analysis Combined with Random Forest for Mineral Prediction in Goulmima, Morocco
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