Trauth MATLAB® Recipes for Earth Sciences

3rd Auflage 2010
ISBN: 978-3-642-12762-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 343 Seiten

MATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models, and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis, and the application of linear time-invariant and adaptive filters. The revised and updated Third Edition includes ten new sections and has greatly expanded on most chapters from the previous edition, including a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Data Storage and Handling, Data Structures and Classes of Objects, Generating M-Files to Regenerate Graphs, Publishing M-Files, Distribution Fitting, Nonlinear and Weighted Regression, Color-Intensity Transects of Varved Sediments, and Grain Size Analysis from Microscope Images. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. All MATLAB recipes can be easily modified in order to analyse the reader's own data sets.

MATLAB® is used for a wide range of applications in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models, and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis, and the application of linear time-invariant and adaptive filters. The revised and updated Third Edition includes ten new sections and has greatly expanded on most chapters from the previous edition, including a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Data Storage and Handling, Data Structures and Classes of Objects, Generating M-Files to Regenerate Graphs, Publishing M-Files, Distribution Fitting, Nonlinear and Weighted Regression, Color-Intensity Transects of Varved Sediments, and Grain Size Analysis from Microscope Images. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. All MATLAB recipes can be easily modified in order to analyse the reader's own data sets.

Autoren/Hrsg.

Trauth, Martin

Weitere Mitwirkende

Sillmann, E.

Inhaltsverzeichnis

1;Preface;5
2;Contents;9
3;1 Data Analysis in Earth Sciences;12
3.1;1.1 Introduction;12
3.2;1.2 Data Collection;12
3.3;1.3 Types of Data;14
3.4;1.4 Methods of Data Analysis;18
3.5;Recommended Reading;20
4;2 Introduction to MATLAB;21
4.1;2.1 MATLAB in Earth Sciences;21
4.2;2.2 Getting Started;22
4.3;2.3 The Syntax;24
4.4;2.4 Data Storage and Handling;28
4.5;2.5 Data Structures and Classes of Objects;31
4.6;2.6 Scripts and Functions;36
4.7;2.7 Basic Visualization Tools;39
4.8;2.8 Generating M-Files to Regenerate Graphs;42
4.9;2.9 Publishing M-Files;45
4.10;Recommended Reading;46
5;3 Univariate Statistics;47
5.1;3.1 Introduction;47
5.2;3.2 Empirical Distributions;47
5.2.1;Measures of Central Tendency;49
5.3;3.3 Example of Empirical Distributions;54
5.4;3.4 Theoretical Distributions;61
5.4.1;Binomial or Bernoulli Distribution;63
5.4.2;Poisson Distribution;64
5.4.3;Normal or Gaussian Distribution;65
5.4.4;Logarithmic Normal or Log-Normal Distribution;66
5.4.5;Student’s t Distribution;67
5.4.6;Fisher’s F Distribution;68
5.4.7;? 2 or Chi-Squared Distribution;69
5.5;3.5 Example of Theoretical Distributions;69
5.6;3.6 The t-Test;71
5.7;3.7 The F-Test;76
5.8;3.8 The ?2-Test;80
5.9;3.9 Distribution Fitting;83
5.10;Recommended Reading;87
6;4 Bivariate Statistics;88
6.1;4.1 Introduction;88
6.2;4.2 Pearson’s Correlation Coeffi cient;89
6.3;4.3 Classical Linear Regression Analysis and Prediction;97
6.4;4.4 Analyzing the Residuals;101
6.5;4.5 Bootstrap Estimates of the Regression Coeffi cients;103
6.6;4.6 Jackknife Estimates of the Regression Coeffi cients;104
6.7;4.7 Cross Validation;107
6.8;4.8 Reduced Major Axis Regression;108
6.9;4.9 Curvilinear Regression;109
6.10;4.10 Nonlinear and Weighted Regression;112
6.11;Recommended Reading;115
7;5 Time-Series Analysis;116
7.1;5.1 Introduction;116
7.2;5.2 Generating Signals;117
7.3;5.3 Auto-Spectral and Cross-Spectral Analysis;121
7.4;5.4 Examples of Auto-Spectral and Cross-Spectral Analysis;126
7.5;5.5 Interpolating and Analyzing Unevenly-Spaced Data;135
7.6;5.6 Evolutionary Power Spectrum;140
7.7;5.7 Lomb-Scargle Power Spectrum;144
7.8;5.8 Wavelet Power Spectrum;148
7.9;5.9 Nonlinear Time-Series Analysis (by N. Marwan);155
7.9.1;Phase Space Portrait;155
7.9.2;Recurrence Plots;161
7.10;Recommended Reading;167
8;6 Signal Processing;169
8.1;6.1 Introduction;169
8.2;6.2 Generating Signals;170
8.3;6.3 Linear Time-Invariant Systems;172
8.4;6.4 Convolution and Filtering;174
8.5;6.5 Comparing Functions for Filtering Data Series;177
8.6;6.6 Recursive and Nonrecursive Filters;180
8.7;6.7 Impulse Response;181
8.8;6.8 Frequency Response;184
8.9;6.9 Filter Design;190
8.10;6.10 Adaptive Filtering;193
8.11;Recommended Reading;199
9;7 Spatial Data;201
9.1;7.1 Types of Spatial Data;201
9.2;7.2 The GSHHS Shoreline Data Set;202
9.3;7.3 The 2-Minute Gridded Global Relief Data ETOPO2;204
9.4;7.4 The 30-Arc Seconds Elevation Model GTOPO30;207
9.5;7.5 The Shuttle Radar Topography Mission SRTM;209
9.6;7.6 Gridding and Contouring Background;212
9.7;7.7 Gridding Example;215
9.8;7.8 Comparison of Methods and Potential Artifacts;219
9.9;7.9 Statistics of Point Distributions;224
9.9.1;Test for Uniform Distribution;224
9.9.2;Test for Random Distribution;227
9.9.3;Test for Clustering;229
9.10;7.10 Analysis of Digital Elevation Models (by R. Gebbers);232
9.11;7.11 Geostatistics and Kriging (by R. Gebbers);243
9.11.1;Theorical Background;243
9.11.2;Preceding Analysis;243
9.11.3;Variography with the Classical Variogram;246
9.11.4;Kriging;255
9.11.5;Discussion of Kriging;260
9.12;Recommended Reading;261
10;8 Image Processing;263
10.1;8.1 Introduction;263
10.2;8.2 Data Storage;264
10.3;8.3 Importing, Processing and Exporting Images;269
10.4;8.4 Importing, Processing and Exporting Satellite Images;274
10.5;8.5 Georeferencing Satellite Images;276
10.6;8.6 Digitizing from the Screen;279
10.7;8.7 Color-Intensity Transects of Varved Sediments;282
10.8;8.8 Grain Size Analysis from Microscope Images;287
10.9;8.9 Quantifying Charcoal in Microscope Images;294
10.10;Recommended Reading;298
11;9 Multivariate Statistics;299
11.1;9.1 Introduction;299
11.2;9.2 Principal Component Analysis;301
11.3;9.3 Independent Component Analysis (by N. Marwan);308
11.4;9.4 Cluster Analysis;312
11.5;Recommended Reading;317
12;10 Statistics on Directional Data;318
12.1;10.1 Introduction;318
12.2;10.2 Graphical Representation;319
12.3;10.3 Empirical Distributions;320
12.4;10.4 Theoretical Distributions;325
12.5;10.5 Test for Randomness of Directional Data;327
12.6;10.6 Test for the Signifi cance of a Mean Direction;328
12.7;10.7 Test for the Diff erence between Two Sets of Directions;329
12.8;Recommended Reading;333
13;General Index;334