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E-raamat: Essentials of Geophysical Data Processing

(University of Texas, Austin)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 21-Oct-2021
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108950480
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Essentials of Geophysical Data ProcessingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 21-Oct-2021
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108950480
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"This textbook provides a concise introduction to geophysical data processing - including many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The treatment begins with calculus before transitioning to discrete time series via the sampling theorem, discussions of aliasing, the use of complex sinusoids, the development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, statistics or Fourier transforms, this textbook draws scenarios and datasets from across the world of geophysics and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises. Exercises are mostly computational in nature and may be completed using MATLAB or a computing environment with similar capabilities"--

A concise survey of essential topics in geophysical data processing for advanced undergraduate and graduate students in geophysics, environmental science, and engineering. With real-life scenarios and datasets, it shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises.

A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual and all the figures from the book as PowerPoints for course instructors.

Arvustused

'This is an excellent textbook for a course on signal processing for 3rd-year geophysics students. Wilson does a great job of taking explanations that are often scattered across engineering books and adapting them towards the needs of geophysicists using relevant examples from the field. It has the perfect balance between depth and simplicity for undergraduates.' Daniel Trad, University of Calgary 'Wilson's book is an excellent compilation of tools and techniques commonly used by practitioners for analyzing various kinds of data arrays. Although the ideas are presented in the context of geophysics, they can be extended to extract information from geochemical and geobiological datasets with the same finesse. The use of real datasets and easy-to-follow explanations of complex mathematical formulations makes the book a good read.' Priyank Jaiswal, Oklahoma State University ' I would recommend this volume not only for graduate and advanced undergraduate students, but for professional geophysicists who could use it as a refresher course.' Patrick Taylor, The Leading Edge

Muu info

Concise, self-contained survey of data processing methods in geophysics and other sciences, for upper level science and engineering students.
Preface ix
1 An Introduction with Geophysical Time Series Examples
1(10)
1.1 Global Mean Sea Level
1(1)
1.2 Stream Discharge
2(2)
1.3 Eastern Pacific Sea Level
4(1)
1.4 El Nino Southern Oscillation (ENSO) Index
4(2)
1.5 Lake Vostok Ice Core Temperature History
6(1)
1.6 Hector Mines Earthquake Seismograms
7(2)
1.7 Simulated Seismograms, White Noise, and Computing Environments
9(1)
1.8
Chapter Summary
9(2)
2 Analog Signals and Digital Time Series
11(14)
2.1 Digital Time Series Notation
11(1)
2.2 Digitizing Analog Signals
12(2)
2.3 Undersampling and Aliasing
14(1)
2.4 Time Series Statistics
15(4)
2.4.1 Mean or Average Value
15(1)
2.4.2 Variance and Standard Deviation
16(1)
2.4.3 Autocorrelation
17(2)
2.5 Numerical Representation of Samples
19(1)
2.6 Decibels
20(1)
2.7 Applications to Digital Audio Recording
21(1)
2.8
Chapter Summary
22(3)
Exercises
23(2)
3 Sinusoids and Fourier Series
25(12)
3.1 Sinusoids
25(1)
3.2 Fourier Series
26(3)
3.3 Partial Fourier Sums
29(1)
3.4 Complex Numbers
29(2)
3.5 Complex Sinusoids
31(2)
3.6
Chapter Summary
33(4)
Exercises
34(3)
4 The Discrete Fourier Transform
37(13)
4.1 Fourier Series in Complex Notation
37(1)
4.2 From Fourier Series to DFT
38(1)
4.3 Frequency and Time Ordering
39(1)
4.4 DFT Normalization Conventions
40(1)
4.5 Sinusoidal Coefficients of a Climate Time Series
40(2)
4.6 FFT Algorithms
42(1)
4.7 Zero-Padding and Interpolation
43(1)
4.8 DFT Interpolation Example
44(1)
4.9 Analytic Signal Computation and Application to Measuring Surface Wave Dispersion
44(3)
4.10
Chapter Summary
47(3)
Exercises
48(2)
5 Linear Systems and Digital Filters
50(11)
5.1 Linear Filter Equations
50(1)
5.2 Discrete Convolution
51(2)
5.3 Correlation
53(1)
5.4 Convolution Matrices
53(1)
5.5 Transfer Functions
54(2)
5.6 Impulse Response
56(1)
5.7 Filter Cascades and Inverses
57(1)
5.8
Chapter Summary
58(3)
Exercises
59(2)
6 Convolution and Related Theorems
61(12)
6.1 Convolution Theorem for the Z Transform
61(1)
6.2 DFT Circular Convolution Theorem
62(1)
6.3 Autocorrelation Theorem
63(1)
6.4 Window Functions
64(4)
6.5 Linear Filtering with the DFT
68(2)
6.6
Chapter Summary
70(3)
Exercises
71(2)
7 Least Squares
73(21)
7.1 Motivations for Least Squares
73(1)
7.2 Least Squares via Maximum Likelihood
74(3)
7.3 Least Squares via Linear Algebra
77(4)
7.4 Weighted Least Squares
81(1)
7.5 Parameter Error Covariance Matrix
82(1)
7.6 Fitting Data to Sinusoids
83(1)
7.7 Ocean Tide Prediction
84(2)
7.8 Seismic Tomography
86(2)
7.9 A Model for Global Sea Level Change
88(3)
7.10
Chapter Summary
91(3)
Exercises
92(2)
8 Linear Filter Design
94(22)
8.1 Introducing the Z Plane
94(2)
8.2 Z Plane Geometry -- Stability and Invertibility
96(2)
8.3 Notch Filter Design Using Z Plane Geometry
98(2)
8.4 Differential Equation to Digital Filter Equation
100(3)
8.5 Derivative and Integration Filters
103(1)
8.6 Echo and Reverberation Filters
104(4)
8.7 Sampled Impulse Response Filter Coefficients
108(1)
8.8 Gravity Anomaly Calculations
109(3)
8.9 Ground Motion Amplification in an Earthquake
112(2)
8.10
Chapter Summary
114(2)
Exercises
114(2)
9 Least Squares and Correlation Filters
116(20)
9.1 Least Squares Inverse Filters
116(1)
9.2 Yule-Walker Equations
117(3)
9.3 Interpolation Filters
120(2)
9.4 Prediction Error Filters
122(1)
9.5 Deconvolution Filters in Reflection Seismology
123(1)
9.6 Power Spectrum Estimate from the PEF
124(1)
9.7 Vibroseis and Matched (Correlation) Filtering
124(3)
9.8 Correlation Filtering in the Global Positioning System
127(2)
9.9 De-Blurring Filter Design
129(4)
9.10
Chapter Summary
133(3)
Exercises
133(3)
10 Power and Coherence Spectra
136(16)
10.1 The DFT Periodogram
136(1)
10.2 Periodogram of White Noise
137(4)
10.3 Comparing Power Spectrum Estimation Methods
141(4)
10.4 Correlation and Coherence
145(1)
10.5 Coherence of Sea Level Variations
146(1)
10.6 Searching for Milankovitch Periods
147(3)
10.7
Chapter Summary
150(2)
Exercises
150(2)
Appendix A Matrices and Vectors 152(3)
Appendix B Fourier Transforms of Continuous Functions 155(14)
Appendix C Random Variable Concepts and Applications 169(19)
Appendix D Further Reading 188(1)
Index 189
Clark R. Wilson is the Carlton Centennial Professor of Geophysics at the University of Texas, Austin. After undergraduate studies in physics (University of California, San Diego) and graduate work in geophysics (Masters and PhD) at Scripps Institution of Oceanography, he joined the faculty of the Department of Geological Sciences at UT Austin. His research has ranged over diverse fields including applied seismology, space geodesy, and hydrology. He has twice served as Department Chair, and spent three years at NASA Headquarters overseeing programs in geodynamics and potential fields.
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