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El. knyga: Basic Environmental Data Analysis for Scientists and Engineers

  • Formatas: 298 pages
  • Išleidimo metai: 22-Nov-2019
  • Leidėjas: CRC Press
  • Kalba: eng
  • ISBN-13: 9781000725612
Kitos knygos pagal šią temą:
Basic Environmental Data Analysis for Scientists and EngineersDidesnis paveikslėlis
  • Formatas: 298 pages
  • Išleidimo metai: 22-Nov-2019
  • Leidėjas: CRC Press
  • Kalba: eng
  • ISBN-13: 9781000725612
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Classroom tested and the result of over 30 years of teaching and research, this textbook is an invaluable tool for undergraduate and graduate data analysis courses in environmental sciences and engineering. It is also a useful reference on modern digital data analysis for the extensive and growing community of Earth scientists and engineers.

Basic Environmental Data Analysis for Scientists and Engineers introduces practical concepts of modern digital data analysis and graphics, including numerical/graphical calculus, measurement units and dimensional analysis, error propagation and statistics, and least squares data modeling. It emphasizes array-based or matrix inversion and spectral analysis using the fast Fourier transform (FFT) that dominates modern data analysis. Divided into two parts, this comprehensive hands-on textbook is excellent for exploring data analysis principles and practice using MATLAB®, Mathematica, Mathcad, and other modern equation solving software.

Part I, for beginning undergraduate students, introduces the basic approaches for quantifying data variations in terms of environmental parameters. These approaches emphasize uses of the data array or matrix, which is the fundamental data and mathematical processing format of modern electronic computing. Part II, for advanced undergraduate and beginning graduate students, extends the inverse problem to least squares solutions involving more than two unknowns.

Features:











Offers a uniquely practical guide for making students proficient in modern electronic data analysis and graphics





Includes topics that are not explained in any existing textbook on environmental data analysis





Data analysis topics are very well organized into a two-semester course that meets general education curriculum requirements in science and engineering





Facilitates learning by beginning each chapter with an Overview section highlighting the topics covered, and ending it with a Key Concepts section summarizing the main technical details that the reader should have acquired





Indexes many numerical examples for ready access in the classroom or other venues serviced by electronic equation solvers like MATLAB®, Mathematica, Mathcad, etc.





Offers supplemental exercises and materials to enhance understanding the principles and practice of modern data analysis
Preface xiii
0.1 Book Objectives and Organization xiii
0.2 Related Books xiv
0.3 Acknowledgments xv
0.4 Author Biography xv
PART I Digital Data Calculus, Units, Errors, Statistics, and Linear Regression
Chapter 1 Computing Trends
3(12)
1.1 Overview
3(1)
1.2 Numeration
4(1)
1.3 Numerical Calculus
4(6)
1.4 Computing Devices
10(3)
1.5 Key Concepts
13(2)
Chapter 2 Data Attributes
15(20)
2.1 Overview
15(1)
2.2 Types
16(1)
2.3 Formats
17(5)
2.3.1 Elementary Matrix Algebra
18(4)
2.4 Units
22(11)
2.4.1 Dimensional Analysis
23(5)
2.4.2 Similitude
28(4)
2.4.3 Summary
32(1)
2.5 Key Concepts
33(2)
Chapter 3 Error Analysis
35(8)
3.1 Overview
35(1)
3.2 Error Types
35(1)
3.3 Accuracy and Precision
36(1)
3.4 Absolute and Relative Errors
36(1)
3.5 Significant Figures
37(1)
3.6 Error Propagation
38(4)
3.7 Key Concepts
42(1)
Chapter 4 Statistics
43(48)
4.1 Overview
43(1)
4.2 Basic Parameters
44(6)
4.2.1 Population Mean, Variance, and Standard Deviation
44(1)
4.2.2 Sample Mean, Variance, and Standard Deviation
45(1)
4.2.3 Variance/Covariance Propagation
46(2)
4.2.4 Probability and Distributions
48(2)
4.3 Binomial Distribution
50(7)
4.3.1 Permutations and Combinations
52(2)
4.3.2 Probability Interpretation
54(3)
4.4 Gaussian or Normal Distribution
57(5)
4.4.1 Probability Interpretation
57(3)
4.4.2 Curve Fitting a Histogram
60(2)
4.4.3 Central Limits Theorem
62(1)
4.5 Statistical Inference
62(27)
4.5.1 Confidence Intervals on the Mean
62(5)
4.5.2 Hypothesis Tests of the Mean
67(2)
4.5.3 Confidence Intervals on the Variance
69(2)
4.5.4 Hypothesis Tests of the Variance
71(3)
4.5.5 Analysis of Variance
74(1)
4.5.5.1 One-way ANOVA
75(3)
4.5.5.2 Two-way ANOVA
78(6)
4.5.6 Distribution Testing
84(2)
4.5.7 Non-Parametric Testing
86(3)
4.6 Key Concepts
89(2)
Chapter 5 Data Sampling
91(8)
5.1 Overview
91(1)
5.2 Introduction
91(1)
5.3 Sampling Considerations
91(3)
5.3.1 Sufficiency
92(1)
5.3.2 Biasing
93(1)
5.3.3 Distribution
93(1)
5.3.4 Errors
93(1)
5.4 Sampling Methods
94(3)
5.4.1 Systematic
95(1)
5.4.2 Random
95(1)
5.4.3 Cluster
96(1)
5.4.4 Selecting a Method
97(1)
5.5 Key Concepts
97(2)
Chapter 6 Algebraic Linear Regression
99(28)
6.1 Overview
99(1)
6.2 Concept of Least Squares
100(1)
6.3 Straight Line Modeling
101(2)
6.4 Measures of the Fit
103(13)
6.4.1 Variances of the Intercept and Slope
103(1)
6.4.1.1 Uniform Error Observations
103(1)
6.4.1.2 Variable Error Observations
104(2)
6.4.2 Confidence Intervals on the Intercept and Slope
106(1)
6.4.3 Confidence Intervals on the Predictions
107(1)
6.4.4 Correlation Coefficient
107(3)
6.4.4.1 Interpretation
110(1)
6.4.4.2 Significance
110(4)
6.4.5 Significance of the Fit
114(2)
6.5 Additional Linear Regression Strategies
116(8)
6.5.1 Weighted Linear Regression
118(1)
6.5.2 Reversed Variables
119(1)
6.5.3 Least Squares Cubic Line
120(2)
6.5.4 Reduced Major Axis Line
122(2)
6.6 Key Concepts
124(3)
Chapter 7 Matrix Linear Regression
127(16)
7.1 Overview
127(1)
7.2 Introduction
128(2)
7.3 Solving for the Unknowns
130(2)
7.3.1 Cramer's Rule
130(1)
7.3.2 Inverse Determination
131(1)
7.3.3 Elimination Methods
131(1)
7.4 Sensitivity Analysis
132(5)
7.4.1 Intercept and Slope Variances
132(3)
7.4.2 Intercept and Slope Error Bars
135(1)
7.4.3 Prediction Variance and Error Bars
135(1)
7.4.4 Correlation Coefficient
136(1)
7.4.5 ANOVA Significance
136(1)
7.5 Additional Linear Regression Strategies
137(1)
7.6 Summary
137(2)
7.7 Key Concepts
139(4)
PART II Digital Data Inversion, Spectral Analysis, Interrogation, and Graphics
Chapter 8 Basic Digital Data Analysis
143(10)
8.1 Overview
143(1)
8.2 Introduction
143(3)
8.3 Data Inversion or Modeling
146(4)
8.4 Trial and Error Methods
150(1)
8.5 Key Concepts
150(3)
Chapter 9 Array Methods
153(24)
9.1 Overview
153(2)
9.2 Introduction
155(1)
9.3 Matrix Inversion
156(10)
9.3.1 Error Statistics
161(2)
9.3.2 Sensitivity Analysis
163(3)
9.4 Generalized Linear Inversion
166(6)
9.4.1 Eigenvalues and Eigenvectors
166(1)
9.4.2 Singular Value Decomposition
167(1)
9.4.3 Generalized Linear Inverse
168(1)
9.4.3.1 Information Density
169(1)
9.4.3.2 Model Resolution
170(1)
9.4.3.3 Model Variance
171(1)
9.4.4 Sensitivity Analysis
172(1)
9.5 Summary
172(2)
9.6 Key Concepts
174(3)
Chapter 10 Spectral Analysis
177(20)
10.1 Overview
177(1)
10.2 Introduction
178(2)
10.3 Analytical Fourier Transform
180(4)
10.4 Numerical Fourier Transform
184(11)
10.4.1 Numerical Errors
191(1)
10.4.1.1 Gibbs' Error
191(2)
10.4.1.2 Wavenumber Leakage
193(1)
10.4.1.3 Wavenumber Aliasing
193(2)
10.4.1.4 Wavenumber Resolution
195(1)
10.5 Key Concepts
195(2)
Chapter 11 Data Interrogation
197(44)
11.1 Overview
197(1)
11.2 Introduction
198(1)
11.3 Convolution
199(12)
11.3.1 Analytical Convolution
199(2)
11.3.2 Numerical Convolution and Deconvolution
201(1)
11.3.3 Convolution and Correlation Theorems
201(8)
11.3.4 Summary
209(2)
11.4 Isolation and Enhancement
211(26)
11.4.1 Spatial Filtering
212(1)
11.4.1.1 Geological Methods
212(1)
11.4.1.2 Graphical Methods
212(1)
11.4.1.3 Trend Surface Analysis
213(3)
11.4.1.4 Analytical Grid Methods
216(1)
11.4.2 Spectral Filtering
217(1)
11.4.2.1 Wavelength Filters
217(5)
11.4.2.2 Directional Filters
222(3)
11.4.2.3 Correlation Filters
225(4)
11.4.2.4 Derivative and Integral Filters
229(4)
11.4.2.5 Interpolation and Continuation Filters
233(4)
11.5 Key Concepts
237(4)
Chapter 12 Data Graphics
241(24)
12.1 Overview
241(1)
12.2 Introduction
242(1)
12.3 Map Projections and Transformations
243(1)
12.4 Gridding
244(12)
12.4.1 Linear Interpolation
245(2)
12.4.2 Cubic Spline Interpolation
247(4)
12.4.3 Equivalent Source Interpolation
251(3)
12.4.4 Polynomial Interpolation
254(1)
12.4.5 Statistical Interpolation
254(2)
12.5 Graphical Parameters and Practice
256(3)
12.5.1 Standardized and Normalized Data
257(1)
12.5.2 Local Favorability Indices
258(1)
12.6 Presentation Modes
259(3)
12.7 Key Concepts
262(3)
References 265(6)
Index 271
Ralph R.B. von Frese is Professor of Earth Sciences at The Ohio State University, where he has taught undergraduate and graduate courses in geomathematics, geophysics, and environmental and earth sciences since 1982. His research has focused mostly on archaeological and planetary applications of gravity and magnetic fields, and he has authored or co-authored an exploration geophysics textbook and more than 125 journal publications including 3 special journal volumes, and served on several government and scientific panels. He is a founding co- chair of the Antarctic Digital Magnetic Anomaly Project (ADMAP), an international collaboration of the Scientific Committee for Antarctic Research (SCAR) and the International Association of Geomagnetism and Aeronomy (IAGA). He is a member of the Society of Exploration Geophysicists, the American Geophysical Union, and the Geological Society of America.
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