MATLAB® is used in a wide range of geoscientific applications, e.g. for image processing in remote sensing, for creating and processing digital elevation models, and for analyzing time series. This book introduces readers to MATLAB-based data analysis methods used in the geosciences, including basic statistics for univariate, bivariate and multivariate datasets, time-series analysis, signal processing, the analysis of spatial and directional data, and image analysis. The revised and updated Fifth Edition includes seven new sections, and the majority of the chapters have been rewritten and significantly expanded. New sections include error analysis, the problem of classical linear regression of log-transformed data, aligning stratigraphic sequences, the Normalized Difference Vegetation Index, Aitchison’s log-ratio transformation, graphical representation of spherical data, and statistics of spherical data. The book also includes numerous examples demonstrating how MATLAB can be used on datasets from the earth sciences. The supplementary electronic material (available online through SpringerLink) contains recipes that include all the MATLAB commands featured in the book and the sample data.
Author(s): Martin H. Trauth
Edition: 5
Publisher: Springer
Year: 2020
Language: English
Pages: 517
City: Cham
Preface to the Fifth Edition
Contents
1 Data Analysis in Earth Sciences
1.1 Introduction
1.2 Data Collection
1.3 Types of Data
1.4 Methods of Data Analysis
Recommended Reading
2 Introduction to MATLAB
2.1 MATLAB in Earth Sciences
2.2 Getting Started
2.3 The Syntax
2.4 Array Manipulation
2.5 Data Types in MATLAB
2.6 Data Storage and Handling
2.7 Control Flow
2.8 Scripts and Functions
2.9 Basic Visualization Tools
2.10 Generating Code to Recreate Graphics
2.11 Publishing and Sharing MATLAB Code
2.12 Creating Graphical User Interfaces
Recommended Reading
3 Univariate Statistics
3.1 Introduction
3.2 Empirical Distributions
Measures of Central Tendency
Measures of Dispersion
3.3 Examples of Empirical Distributions
3.4 Theoretical Distributions
Uniform Distribution
Binomial or Bernoulli Distribution
Poisson Distribution
Normal or Gaussian Distribution
Logarithmic Normal or Log-Normal Distribution
Student’s t Distribution
Fisher’s F Distribution
χ2 or Chi-Squared Distribution
3.5 Examples of Theoretical Distributions
3.6 Hypothesis Testing
3.7 The t-Test
3.8 The F-Test
3.9 The χ2-Test
3.10 The Kolmogorov-Smirnov Test
3.11 Mann-Whitney Test
3.12 The Ansari-Bradley Test
3.13 Distribution Fitting
3.14 Error Analysis
Recommended Reading
4 Bivariate Statistics
4.1 Introduction
4.2 Correlation Coefficients
4.3 Classical Linear Regression Analysis
4.4 Analyzing the Residuals
4.5 Bootstrap Estimates of the Regression Coefficients
4.6 Jackknife Estimates of the Regression Coefficients
4.7 Cross Validation
4.8 Reduced Major Axis Regression
4.9 Curvilinear Regression
4.10 Nonlinear and Weighted Regression
4.11 Classical Linear Regression of Log-Transformed Data
Recommended Reading
5 Time-Series Analysis
5.1 Introduction
5.2 Generating Signals
5.3 Auto-Spectral and Cross-Spectral Analysis
5.4 Examples of Auto-Spectral and Cross-Spectral Analysis
5.5 Interpolating and Analyzing Unevenly-Spaced Data
5.6 Evolutionary Power Spectrum
5.7 Lomb-Scargle Power Spectrum
5.8 Wavelet Power Spectrum
5.9 Detecting Abrupt Transitions in Time Series
5.10 Aligning Stratigraphic Sequences
5.11 Nonlinear Time-Series Analysis (by N. Marwan)
Phase Space Portrait
Recurrence Plots
Recurrence Quantification
Recommended Reading
6 Signal Processing
6.1 Introduction
6.2 Generating Signals
6.3 Linear Time-Invariant Systems
6.4 Convolution, Deconvolution and Filtering
6.5 Comparing Functions for Filtering Data Series
6.6 Recursive and Nonrecursive Filters
6.7 Impulse Response
6.8 Frequency Response
6.9 Filter Design
6.10 Adaptive Filtering
Recommended Reading
7 Spatial Data
7.1 Types of Spatial Data
7.2 The Global Geography Database GSHHG
7.3 The 1 Arc-Minute Gridded Global Relief Data ETOPO1
7.4 The 30 Arc-Seconds Elevation Model GTOPO30
7.5 The Shuttle Radar Topography Mission SRTM
7.6 Exporting 3D Graphics to Create Interactive Documents
7.7 Gridding and Contouring
7.8 Comparison of Methods and Potential Artifacts
7.9 Statistics of Point Distributions
Test for Uniform Distribution
Test for Random Distribution
Test for Clustering
7.10 Analysis of Digital Elevation Models (by R. Gebbers)
7.11 Geostatistics and Kriging (by R. Gebbers)
Theorical Background
Preceding Analysis
Variography with the Classic Variogram
Kriging
Discussion of Kriging
Recommended Reading
8 Image Processing
8.1 Introduction
8.2 Data Storage
8.3 Importing, Processing and Exporting Images
8.4 Importing, Processing and Exporting LANDSAT Images
8.5 Importing and Georeferencing TERRA ASTER Images
8.6 Processing and Exporting EO-1 Hyperion Images
8.7 Digitizing from the Screen
8.8 Image Enhancement, Correction and Rectification
8.9 Color-Intensity Transects Across Varved Sediments
8.10 Grain Size Analysis from Microscope Images
8.11 Quantifying Charcoal in Microscope Images
8.12 Shape-Based Object Detection in Images
8.13 The Normalized Difference Vegetation Index
Recommended Reading
9 Multivariate Statistics
9.1 Introduction
9.2 Principal Component Analysis
9.3 Independent Component Analysis (by N. Marwan)
9.4 Discriminant Analysis
9.5 Cluster Analysis
9.6 Multiple Linear Regression
9.7 Aitchison’s Log-Ratio Transformation
Recommended Reading
10 Directional Data
10.1 Introduction
10.2 Graphical Representation of Circular Data
10.3 Empirical Distributions of Circular Data
10.4 Theoretical Distributions of Circular Data
10.5 Test for Randomness of Circular Data
10.6 Test for the Significance of a Mean Direction
10.7 Test for the Difference between Two Sets of Directions
10.8 Graphical Representation of Spherical Data
10.9 Statistics of Spherical Data
Recommended Reading
