Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser

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5.3. SYNTHETIC DATA EXAMPLES 81 Time (s) Time (s) 0 0.2 0.4 0.6 0 0.2 0.4 0.6 Traces (a) Traces (d) 50 50 0 0.2 0.4 0.6 0 0.2 0.4 0.6 Traces (b) Traces (e) 50 50 0 0.2 0.4 0.6 0 0.2 0.4 0.6 Traces (c) Traces Figure 5.4: Synthetic data example with two multiples and one primary. (a) Original data. (b) Adaptive multiple attenuation via f−x linear prediction error filtering. (c) Adaptive multiple attenuation obtained with a f − x nonlinear prediction error operator (third order Volterra series). (d) Prediction of multiples. (e) Removed multiples via f − x linear PEF. (f) Removed multiples with a third- order Volterra PEF. (f) 50 50
5.3. SYNTHETIC DATA EXAMPLES 82 Time (s) Time (s) 0 0.2 0.4 0.6 0 0.2 0.4 0.6 Traces 20 40 (a) Traces 20 40 (c) Time (s) Time (s) 0 0.2 0.4 0.6 0 0.2 0.4 0.6 Traces 20 40 (b) Traces 20 40 Figure 5.5: Synthetic data example with two multiples and three primaries. (a) Original data. (b) Prediction of multiples. (c) Adaptive multiple attenuation via f − x linear prediction error filtering. (d) Adaptive multiple attenuation obtained with a f − x nonlinear prediction error operator (third order Volterra series). (d)
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Name of Author: Soner Bekleric Univ
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University of Alberta Faculty of Gr
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Abstract Linear filter theory has p
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Contents 1 Introduction 1 1.1 Appli
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List of Tables 3.1 Filter length an
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4.4 (a) Prediction of Figure 4.2(b)
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List of symbols Symbol Name or desc
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List of abbreviations Abbreviation
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In applied seismology predictive de
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1.1. APPLICATIONS 4 applied to vide
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1.3. THESIS OUTLINE 6 • Chapter 4
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2.2. LINEAR PROCESS 8 Finally, I pr
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2.3. LINEAR PREDICTION 10 E(z) 1 X(
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2.3. LINEAR PREDICTION 12 In my the
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2.3. LINEAR PREDICTION 14 which can
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2.3. LINEAR PREDICTION 16 ∂ρ fb
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2.3. LINEAR PREDICTION 18 I have an
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2.4. 1-D SYNTHETIC AND REAL DATA EX
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2.4. 1-D SYNTHETIC AND REAL DATA EX
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2.6. SUMMARY 24 Relative PSD Relati
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Chapter 3 Nonlinear Prediction 3.1
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3.1. NONLINEAR PROCESSES VIA THE VO
Page 45 and 46: 3.1. NONLINEAR PROCESSES VIA THE VO
Page 47 and 48: 3.2. NONLINEAR MODELING OF TIME SER
Page 55 and 56: 3.3. 1-D SYNTHETIC AND REAL DATA EX
Page 61 and 62: 3.4. SUMMARY 46 3.4 Summary In this
Page 63 and 64: 4.1. LINEAR PREDICTION IN THE F −
Page 65 and 66: 4.2. ANALYSIS OF OPTIMUM FILTER LEN
Page 67 and 68: 4.2. ANALYSIS OF OPTIMUM FILTER LEN
Page 69 and 70: 4.3. NONLINEAR PREDICTION OF COMPLE
Page 73 and 74: RMSE 2 4.3. NONLINEAR PREDICTION OF
Page 77 and 78: 4.4. NOISE REMOVAL AND VOLTERRA SER
Page 79 and 80: 4.5. SYNTHETIC AND REAL DATA EXAMPL
Page 91 and 92: 5.1. INTRODUCTION 76 ples remains a
Page 93 and 94: 5.3. SYNTHETIC DATA EXAMPLES 78 Not
Page 95: 5.3. SYNTHETIC DATA EXAMPLES 80 Tim
Page 99 and 100: 5.4. REAL DATA EXAMPLES 84 Time [s]
Page 107 and 108: series in this thesis has been trun
Page 109 and 110: 6.1. FUTURE DIRECTIONS 94 Noise red
Page 111 and 112: BIBLIOGRAPHY 96 Canales, L. L. (198
Page 113 and 114: BIBLIOGRAPHY 98 Marple, S. L. (1980
Page 115 and 116: BIBLIOGRAPHY 100 Trad, D. (2003). I
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Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser

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