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Introduction to Mining Geostatistics : Intuitive Applications With Excel and R

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Introduction to Mining Geostatistics : Intuitive Applications With Excel and R
Περιγραφή: Introduction to Mining Geostatistics: Intuitive Applications with Excel and R is a practical and accessible guide to geostatistical techniques in mineral exploration, with a strong focus on reserves estimation. Designed for students, researchers, and industry professionals, this book blends fundamental concepts of theory with hands-on applications, using Excel and R to simplify complex analyses.Key topics include:Essential Statistical Foundations – Master core data analysis techniques for ore reserves estimation.Sampling Strategies & Error Analysis – Minimize uncertainty and improve data reliability.Spatial Analysis & Kriging – Use variograms, covariance functions, and Kriging algorithms to estimate unknown values from borehole data.Multivariate Geostatistics – Model interdependent variables to enhance accuracy and predictive power.Stochastic Simulation – Explore alternative estimation methods for risk assessment and scenario analysis.Reserve Classification & Reporting – Understand global classification systems and key reserve estimation parameters.Filled with real-world case studies and practical examples, this book bridges theory and application, making geostatistics intuitive and approachable. Whether you're optimizing exploration projects, improving resource estimates, or conducting economic risk assessments, this guide equips you with the tools to make informed decisions. - Includes templated spreadsheet examples and exercises in Excel and R for accessible understanding - Provides geometric instead of algebraic representation wherever possible - Detailed visualization of geostatistics theory throughout the chapters
Συγγραφείς: Konstantinos Modis, George Valakas
Resource Type: eBook.
Θέματα: Mining geology--Statistical methods, Electronic spreadsheets--Computer programs, R (Computer program language)
Categories: SCIENCE / Earth Sciences / Geology, BUSINESS & ECONOMICS / Industries / Natural Resource Extraction
Βάση Δεδομένων: eBook Index
FullText Text:
  Availability: 0
Header DbId: edsebk
DbLabel: eBook Index
An: 4375021
RelevancyScore: 981
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 981.043701171875
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  Data: Introduction to Mining Geostatistics: Intuitive Applications with Excel and R is a practical and accessible guide to geostatistical techniques in mineral exploration, with a strong focus on reserves estimation. Designed for students, researchers, and industry professionals, this book blends fundamental concepts of theory with hands-on applications, using Excel and R to simplify complex analyses.Key topics include:Essential Statistical Foundations – Master core data analysis techniques for ore reserves estimation.Sampling Strategies & Error Analysis – Minimize uncertainty and improve data reliability.Spatial Analysis & Kriging – Use variograms, covariance functions, and Kriging algorithms to estimate unknown values from borehole data.Multivariate Geostatistics – Model interdependent variables to enhance accuracy and predictive power.Stochastic Simulation – Explore alternative estimation methods for risk assessment and scenario analysis.Reserve Classification & Reporting – Understand global classification systems and key reserve estimation parameters.Filled with real-world case studies and practical examples, this book bridges theory and application, making geostatistics intuitive and approachable. Whether you're optimizing exploration projects, improving resource estimates, or conducting economic risk assessments, this guide equips you with the tools to make informed decisions. - Includes templated spreadsheet examples and exercises in Excel and R for accessible understanding - Provides geometric instead of algebraic representation wherever possible - Detailed visualization of geostatistics theory throughout the chapters
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      – Code: eng
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            – D: 01
              M: 01
              Type: published
              Y: 2025
            – D: 13
              M: 01
              Type: profile
              Y: 2026
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