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MATLAB® Recipes for Earth Sciences 4th ed. 2015 [Kõva köide]

  • Formaat: Hardback, 427 pages, kõrgus x laius: 235x155 mm, kaal: 828 g, 20 Illustrations, color; 96 Illustrations, black and white; XIV, 427 p. 116 illus., 20 illus. in color., 1 Hardback
  • Ilmumisaeg: 13-Apr-2015
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662462435
  • ISBN-13: 9783662462430
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  • Formaat: Hardback, 427 pages, kõrgus x laius: 235x155 mm, kaal: 828 g, 20 Illustrations, color; 96 Illustrations, black and white; XIV, 427 p. 116 illus., 20 illus. in color., 1 Hardback
  • Ilmumisaeg: 13-Apr-2015
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662462435
  • ISBN-13: 9783662462430

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, time-series analysis, signal processing, the analysis of spatial and directional data and image analysis. The revised and updated Fourth Edition includes sixteen new sections and most chapters have greatly been expanded so that they now include a step by step discussion of all methods before demonstrating the methods with MATLAB functions. New sections include: Array Manipulation; Control Flow; Creating Graphical User Interfaces; Hypothesis Testing; Kolmogorov-Smirnov Test; Mann-Whitney Test; Ansari-Bradley Test; Detecting Abrupt Transitions in Time Series; Exporting 3D Graphics to Create Interactive Documents; Importing, Processing and Exporting LANDSAT Images; Importing and Georeferencing TERRA ASTER Images; Processing and Exporting EO-1 Hyperion Images; Image Enhancement; Correction and Rectification; Shape-Based Object Detection in Images; Discriminant Analysis; and Multiple Linear Regression. The text includes numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. The book’s supplementary electronic material (available online through Springer Link) includes recipes that include all the MATLAB commands featured in the book and the example data.

Arvustused

This book uses MATLAB for the representation of, analysis of, and other operations on earth science datasets. the examples in the book are self-explanatory and can be easily understood by those working in the field. Trauth considers undergraduates, post-graduates, doctoral students, scientists, and researchers as the possible audience of this book. (Lalit Saxena, Computing Reviews, September, 2015)

Preface to the 4th Edition v
Preface to the Interactive 4th Edition ix
1 Data Analysis in Earth Sciences
1(10)
1.1 Introduction
1(1)
1.2 Data Collection
2(1)
1.3 Types of Data
3(4)
1.4 Methods of Data Analysis
7(4)
Recommended Reading
9(2)
2 Introduction to MATLAB
11(46)
2.1 MATLAB in Earth Sciences
11(1)
2.2 Getting Started
12(2)
2.3 The Syntax
14(4)
2.4 Array Manipulation
18(6)
2.5 Data Structures and Classes of Objects
24(7)
2.6 Data Storage and Handling
31(6)
2.7 Control Flow
37(4)
2.8 Scripts and Functions
41(4)
2.9 Basic Visualization Tools
45(3)
2.10 Generating Code to Recreate Graphics
48(2)
2.11 Publishing M-Files
50(1)
2.12 Creating Graphical User Interfaces
51(6)
Recommended Reading
55(2)
3 Univariate Statistics
57(64)
3.1 Introduction
57(1)
3.2 Empirical Distributions
58(6)
3.3 Examples of Empirical Distributions
64(10)
3.4 Theoretical Distributions
74(8)
3.5 Examples of Theoretical Distributions
82(6)
3.6 Hypothesis Testing
88(1)
3.7 The t-Test
89(4)
3.8 The F-Test
93(4)
3.9 The χ2-Test
97(3)
3.10 The Kolmogorov-Smirnov Test
100(3)
3.11 Mann-Whitney Test
103(6)
3.12 The Ansari-Bradley Test
109(6)
3.13 Distribution Fitting
115(6)
Recommended Reading
119(2)
4 Bivariate Statistics
121(30)
4.1 Introduction
121(1)
4.2 Correlation Coefficients
122(9)
4.3 Classical Linear Regression Analysis
131(4)
4.4 Analyzing the Residuals
135(2)
4.5 Bootstrap Estimates of the Regression Coefficients
137(1)
4.6 Jackknife Estimates of the Regression Coefficients
138(2)
4.7 Cross Validation
140(1)
4.8 Reduced Major Axis Regression
141(2)
4.9 Curvilinear Regression
143(2)
4.10 Nonlinear and Weighted Regression
145(6)
Recommended Reading
148(3)
5 Time-Series Analysis
151(64)
5.1 Introduction
151(1)
5.2 Generating Signals
152(5)
5.3 Auto-Spectral and Cross-Spectral Analysis
157(4)
5.4 Examples of Auto-Spectral and Cross-Spectral Analysis
161(9)
5.5 Interpolating and Analyzing Unevenly-Spaced Data
170(5)
5.6 Evolutionary Power Spectrum
175(4)
5.7 Lomb-Scargle Power Spectrum
179(5)
5.8 Wavelet Power Spectrum
184(8)
5.9 Detecting Abrupt Transitions in Time Series
192(3)
5.10 Nonlinear Time-Series Analysis (by N. Marwan)
195(20)
Recommended Reading
211(4)
6 Signal Processing
215(34)
6.1 Introduction
215(2)
6.2 Generating Signals
217(1)
6.3 Linear Time-Invariant Systems
218(2)
6.4 Convolution, Deconvolution and Filtering
220(4)
6.5 Comparing Functions for Filtering Data Series
224(2)
6.6 Recursive and Nonrecursive Filters
226(2)
6.7 Impulse Response
228(3)
6.8 Frequency Response
231(6)
6.9 Filter Design
237(3)
6.10 Adaptive Filtering
240(9)
Recommended Reading
248(1)
7 Spatial Data
249(66)
7.1 Types of Spatial Data
249(1)
7.2 The Global Geography Database GSHHG
250(2)
7.3 The 1-Minute Gridded Global Relief Data ETOPOI
252(3)
7.4 The 30-Arc Seconds Elevation Model GTOPO30
255(2)
7.5 The Shuttle Radar Topography Mission SRTM
257(3)
7.6 Exporting 3D Graphics to Create Interactive Documents
260(4)
7.7 Gridding and Contouring
264(7)
7.8 Comparison of Methods and Potential Artifacts
271(7)
7.9 Statistics of Point Distributions
278(7)
7.10 Analysis of Digital Elevation Models (by R. Gebbers)
285(10)
7.11 Geostatistics and Kriging (by R. Gebbers)
295(20)
Recommended Reading
313(2)
8 Image Processing
315(60)
8.1 Introduction
315(1)
8.2 Data Storage
316(6)
8.3 Importing, Processing and Exporting Images
322(4)
8.4 Importing, Processing and Exporting LANDSAT Images
326(5)
8.5 Importing and Georeferencing TERRA ASTER Images
331(7)
8.6 Processing and Exporting EO-1 Hyperion Images
338(5)
8.7 Digitizing from the Screen
343(2)
8.8 Image Enhancement, Correction and Rectification
345(7)
8.9 Color-Intensity Transects Across Varved Sediments
352(5)
8.10 Grain Size Analysis from Microscope Images
357(7)
8.11 Quantifying Charcoal in Microscope Images
364(3)
8.12 Shape-Based Object Detection in Images
367(8)
Recommended Reading
373(2)
9 Multivariate Statistics
375(38)
9.1 Introduction
375(2)
9.2 Principal Component Analysis
377(9)
9.3 Independent Component Analysis (by N. Marwan)
386(6)
9.4 Discriminant Analysis
392(6)
9.5 Cluster Analysis
398(4)
9.6 Multiple Linear Regression
402(11)
Recommended Reading
411(2)
10 Directional Data
413
10.1 Introduction
413(2)
10.2 Graphical Representation
415(2)
10.3 Empirical Distributions
417(2)
10.4 Theoretical Distributions
419(2)
10.5 Test for Randomness of Directional Data
421(1)
10.6 Test for the Significance of a Mean Direction
422(2)
10.7 Test for the Difference between Two Sets of Directions
424
Recommended Reading
427
Martin Trauth, Universität Potsdam, Potsdam, Germany.