Basic Principles of Geostatistical Geospatial Modeling

Case Western Reserve University via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

1 day 14 hours 28 minutes
Sessions

On-Demand
Subtitles

English

Found in

Part of

Practical Geospatial Geostatistical Modeling

Overview

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Ready to harness the power of geostatistics for your data? In this Geospatial Specialization Course #1: Basic Principles of Geostatistical Geospatial Modeling course, you’ll learn how to identify key variables, address outliers and missing data, and apply univariate and bivariate analyses—all within the versatile R programming environment. You’ll quickly master correlation and covariance matrices, construct dynamic visualizations (histograms, boxplots, crossplots), and uncover insights hidden in your spatial datasets. Next, you’ll dive into advanced geostatistical techniques, such as building omnidirectional and directional variograms, developing nested models, and performing kriging and co-kriging for precise spatial predictions. You’ll even explore conditional simulation to capture the full range of possible outcomes. Rigorous post-processing methods—including cross-validation, error variance mapping, and isoprobability analyses—let you confidently validate and refine your models. Whether you’re tackling environmental or mining data (or anything in between), you’ll finish the course with a powerful geostatistical toolbox and the know-how to apply it. Join us and discover how R-powered geostatistical modeling can transform raw data into actionable intelligence!

Syllabus

Introduction

In this introductory module, you'll meet your instructor and discover the power of geostatistical modeling in fields like environmental science and mining. We'll outline the course structure, covering key topics, practical applications, and progress measurement to ensure your success. Let's get started!

Exploratory Data Analytics

In this module, we will explore the fundamental steps and purposes of Exploratory Data Analysis (EDA). EDA is essential for summarizing the main characteristics of data and uncovering patterns, often using visual methods. This module will equip you with the skills to construct univariate and bivariate graphic summaries, use univariate and bivariate statistics to characterize data distributions and relationships, and create data transforms for multivariate statistical and geostatistical methods. Imagine you are a geologist analyzing soil samples from different locations to determine mineral content. EDA will help you visualize the distribution of mineral concentrations, identify any outliers or missing data, and understand the relationships between different variables. By the end of this module, you will have a solid foundation in EDA, enabling you to prepare and analyze data effectively for spatial modeling and other advanced analyses.

Spatial Modeling

In this module, we will explore the essential concepts and techniques of spatial modeling in geostatistical analysis. Spatial modeling is crucial for understanding and predicting spatial patterns and relationships in data. This module will equip you with the skills to construct various types of variograms and apply them in spatial analysis. Throughout this module, you will learn to explain the purpose and necessity of spatial modeling, construct experimental omnidirectional and directional variograms, and develop nested variogram models. By the end of this module, you will have a comprehensive understanding of spatial modeling, enabling you to perform geostatistical analyses with confidence.

Kriging

In this module, we will explore the powerful geostatistical technique of Kriging, which offers significant advantages over other interpolation methods. Kriging is essential for making accurate spatial predictions and understanding spatial variability. Throughout this module, you will learn to explain the rationale of Kriging, perform co-located co-kriging, conduct cross-validation on kriged maps or volumes, construct error variance maps, and develop variogram models for directional regionalized variables. Kriging has numerous real-world applications in geology, such as estimating mineral reserves, mapping subsurface structures, and predicting the distribution of geological features. By the end of this module, you will have a comprehensive understanding of Kriging, enabling you to apply this technique effectively in your geostatistical analyses and make informed decisions in geological studies.

Simulation and Post-Processing

In this final module of the course, we will explore the advanced techniques of conditional simulation and post-processing in geostatistical analysis. These methods are essential for understanding and managing spatial uncertainty in geological data. Throughout this module, you will learn to explain the difference between kriging and conditional simulation, perform normal score transforms, construct conditionally simulated maps and volumes, and create multiple realizations of simulated variables. Additionally, you will delve into post-processing techniques to assess and refine stochastically simulated geostatistical models. By the end of this module, you will have a comprehensive understanding of simulation and post-processing, enabling you to apply these techniques effectively in geological studies and make informed decisions based on spatial uncertainty.