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MATLAB® Recipes for Earth Sciences Fifth Edition 2021 [Kõva köide]

  • Formaat: Hardback, 517 pages, kõrgus x laius: 235x155 mm, kaal: 1105 g, 122 Illustrations, color; 20 Illustrations, black and white; XII, 517 p. 142 illus., 122 illus. in color., 1 Hardback
  • Sari: Springer Textbooks in Earth Sciences, Geography and Environment
  • Ilmumisaeg: 03-Dec-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030384403
  • ISBN-13: 9783030384401
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MATLAB® Recipes for Earth Sciences Fifth Edition 2021Suurem pilt
  • Formaat: Hardback, 517 pages, kõrgus x laius: 235x155 mm, kaal: 1105 g, 122 Illustrations, color; 20 Illustrations, black and white; XII, 517 p. 142 illus., 122 illus. in color., 1 Hardback
  • Sari: Springer Textbooks in Earth Sciences, Geography and Environment
  • Ilmumisaeg: 03-Dec-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030384403
  • ISBN-13: 9783030384401
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030384401.html

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.

1 Data Analysis in Earth Sciences
1(8)
1.1 Introduction
1(1)
1.2 Data Collection
2(1)
1.3 Types of Data
3(3)
1.4 Methods of Data Analysis
6(2)
Recommended Reading
8(1)
2 Introduction to MATLAB
9(60)
2.1 MATLAB in Earth Sciences
9(1)
2.2 Getting Started
10(2)
2.3 The Syntax
12(5)
2.4 Array Manipulation
17(6)
2.5 Data Types in MATLAB
23(15)
2.6 Data Storage and Handling
38(9)
2.7 Control Flow
47(5)
2.8 Scripts and Functions
52(4)
2.9 Basic Visualization Tools
56(3)
2.10 Generating Code to Recreate Graphics
59(2)
2.11 Publishing and Sharing MATLAB Code
61(3)
2.12 Creating Graphical User Interfaces
64(4)
Recommended Reading
68(1)
3 Univariate Statistics
69(76)
3.1 Introduction
69(1)
3.2 Empirical Distributions
70(5)
3.3 Examples of Empirical Distributions
75(11)
3.4 Theoretical Distributions
86(8)
3.5 Examples of Theoretical Distributions
94(5)
3.6 Hypothesis Testing
99(2)
3.7 The T-Test
101(5)
3.8 The F-Test
106(4)
3.9 The x2-Test
110(4)
3.10 The Kolmogorov-Smirnov Test
114(3)
3.11 Mann-Whitney Test
117(7)
3.12 The Ansari-Bradley Test
124(7)
3.13 Distribution Fitting
131(4)
3.14 Error Analysis
135(8)
Recommended Reading
143(2)
4 Bivariate Statistics
145(32)
4.1 Introduction
145(1)
4.2 Correlation Coefficients
146(9)
4.3 Classical Linear Regression Analysis
155(3)
4.4 Analyzing the Residuals
158(3)
4.5 Bootstrap Estimates of the Regression Coefficients
161(1)
4.6 Jackknife Estimates of the Regression Coefficients
162(2)
4.7 Cross Validation
164(1)
4.8 Reduced Major Axis Regression
165(1)
4.9 Curvilinear Regression
166(3)
4.10 Nonlinear and Weighted Regression
169(4)
4.11 Classical Linear Regression of Log-Transformed Data
173(2)
Recommended Reading
175(2)
5 Time-Series Analysis
177(82)
5.1 Introduction
177(1)
5.2 Generating Signals
178(5)
5.3 Auto-Spectral and Cross-Spectral Analysis
183(4)
5.4 Examples of Auto-Spectral and Cross-Spectral Analysis
187(9)
5.5 Interpolating and Analyzing Unevenly-Spaced Data
196(4)
5.6 Evolutionary Power Spectrum
200(5)
5.7 Lomb-Scargle Power Spectrum
205(5)
5.8 Wavelet Power Spectrum
210(6)
5.9 Detecting Abrupt Transitions in Time Series
216(10)
5.10 Aligning Stratigraphic Sequences
226(11)
5.11 Nonlinear Time-Series Analysis
237(18)
N. Marwan
Recommended Reading
255(4)
6 Signal Processing
259(34)
6.1 Introduction
259(1)
6.2 Generating Signals
260(1)
6.3 Linear Time-Invariant Systems
261(3)
6.4 Convolution, Deconvolution and Filtering
264(4)
6.5 Comparing Functions for Filtering Data Series
268(2)
6.6 Recursive and Nonrecursive Filters
270(2)
6.7 Impulse Response
272(2)
6.8 Frequency Response
274(7)
6.9 Filter Design
281(3)
6.10 Adaptive Filtering
284(7)
Recommended Reading
291(2)
7 Spatial Data
293(72)
7.1 Types of Spatial Data
293(1)
7.2 The Global Geography Database GSHHG
294(4)
7.3 The 1 Arc-Minute Gridded Global Relief Data ETOPOl
298(3)
7.4 The 30 Arc-Seconds Elevation Model GTOPO30
301(3)
7.5 The Shuttle Radar Topography Mission SRTM
304(5)
7.6 Exporting 3D Graphics to Create Interactive Documents
309(5)
7.7 Gridding and Contouring
314(6)
7.8 Comparison of Methods and Potential Artifacts
320(7)
7.9 Statistics of Point Distributions
327(8)
7.10 Analysis of Digital Elevation Models
335(10)
R. Gebbers
7.11 Geostatistics and Kriging
345(17)
R. Gebbers
Recommended Reading
362(3)
8 Image Processing
365(74)
8.1 Introduction
365(1)
8.2 Datastorage
366(5)
8.3 Importing, Processing and Exporting Images
371(7)
8.4 Importing, Processing and Exporting LANDSAT Images
378(4)
8.5 Importing and Georeferencing TERRA ASTER Images
382(8)
8.6 Processing and Exporting EO-1 Hyperion Images
390(6)
8.7 Digitizing from the Screen
396(2)
8.8 Image Enhancement, Correction and Rectification
398(8)
8.9 Color-Intensity Transects Across Varved Sediments
406(5)
8.10 Grain Size Analysis from Microscope Images
411(8)
8.11 Quantifying Charcoal in Microscope Images
419(4)
8.12 Shape-Based Object Detection in Images
423(7)
8.13 The Normalized Difference Vegetation Index
430(7)
Recommended Reading
437(2)
9 Multivariate Statistics
439(52)
9.1 Introduction
439(2)
9.2 Principal Component Analysis
441(17)
9.3 Independent Component Analysis
458(6)
N. Marwan
9.4 Discriminant Analysis
464(6)
9.5 Cluster Analysis
470(5)
9.6 Multiple Linear Regression
475(9)
9.7 Aitchison's Log-Ratio Transformation
484(4)
Recommended Reading
488(3)
10 Directional Data
491
10.1 Introduction
491(2)
10.2 Graphical Representation of Circular Data
493(2)
10.3 Empirical Distributions of Circular Data
495(4)
10.4 Theoretical Distributions of Circular Data
499(3)
10.5 Test for Randomness of Circular Data
502(1)
10.6 Test for the Significance of a Mean Direction
503(3)
10.7 Test for the Difference between Two Sets of Directions
506(3)
10.8 Graphical Representation of Spherical Data
509(4)
10.9 Statistics of Spherical Data
513(3)
Recommended Reading
516
Martin H. Trauth studied geophysics and geology at the University of Karlsruhe. He obtained a doctoral degree from the University of Kiel in 1995 and then became a permanent member of the scientific staff at the University of Potsdam. Following his habilitation in 2003 he became a lecturer, and then in 2011 a titular professor at the University of Potsdam. Since 1990 he has worked on various aspects of past changes in the climates of East Africa and South America. His projects have aimed to understand the role of the tropics in terminating ice ages, the relationship between climatic changes and human evolution, and the influence that climate anomalies had on mass movements in the central Andes. Each of these projects has involved the use of MATLAB to apply numerical and statistical methods (such as time-series analysis and signal processing) to paleoclimate time series, lake-balance modeling, stochastic modeling of bioturbation, age-depth modeling of sedimentary sequences, or the processing of satellite and microscope images. Martin H. Trauth has been teaching a variety of courses on data analysis in earth sciences with MATLAB for more than 25 years, both at the University of Potsdam and at other universities around the world.