| Description |
1 online resource |
| Contents |
Front Cover; Complexity of Seismic Time Series; Copyright Page; Contents; List of Contributors; Foreword; Introduction; References; I. Complexity Measurement in Seismograms and Natural and Artificial Time Series of EQs (Catalogs); 1 Analysis of the Complexity of Seismic Data Sets: Case Study for Caucasus; 1.1 Introduction; 1.2 Data; 1.2.1 Waiting Times and Earthquake Interdistances; 1.2.2 Ambient Seismic Noise; 1.3 Methods of Analysis; 1.4 Results of Analysis; 1.4.1 Waiting Times and Earthquake Interdistance Analysis; 1.4.2 Ambient Seismic Noise Data Analysis; 1.5 Conclusions; Acknowledgement |
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2.5.1 The Frequency-Magnitude Distribution of Seismicity2.5.2 Temporal Variations and the Evolution of Seismicity; 2.5.3 Spatiotemporal Properties of Seismicity and Nonextensive Statistical Mechanics; 2.5.3.1 Spatial Properties of Seismicity; 2.5.3.2 Temporal Properties of Seismicity and the Risk Function; 2.6 Discussion -- Quo Vademus?; Acknowledgements; References; 3 Spatiotemporal Clustering of Seismic Occurrence and Its Implementation in Forecasting Models; 3.1 Introduction; 3.2 A Physical Interpretation of the ETAS Model; 3.2.1 Rate-and-State Friction |
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3.2.2 Viscous Coupling With the Asthenosphere3.3 Short-Term Aftershock Incompleteness and Its Implementation in the ETAS Model; 3.3.1 Mechanisms Responsible for STAI; 3.3.2 The Dynamic Scaling ETAS Model; 3.3.3 The ETAS Incomplete Model; 3.4 Foreshock Occurrence in the ETAS Model; 3.4.1 The Foreshock Productivity Law and the Inverse Omori Law; 3.4.2 The Foreshock Spatial Distribution; 3.4.3 The ETAFS Model; 3.5 Numerical Implementation of the ETAS Model; References; 4 Fractal, Informational and Topological Methods for the Analysis of Discrete and Continuous Seismic Time Series: An Overview |
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4.1 Introduction4.2 Fractal Methods; 4.2.1 Coefficient of Variation; 4.2.2 Detrended Fluctuation Analysis; 4.2.3 Multifractal Detrended Fluctuation Analysis; 4.2.4 Allan Factor; 4.3 Informational Methods; 4.4 Topological Methods; 4.5 Conclusions; References; 5 Modelling of Persistent Time Series by the Nonlinear Langevin Equation; 5.1 Introduction; 5.2 Modified Langevin Equation; 5.3 Reconstruction Procedures; 5.3.1 Modified Numerical Reconstruction Procedure (MNRP); 5.3.2 Modified Semianalytical Reconstruction Procedure (MsARP); 5.4 Testing of the Reconstruction Procedures; 5.5 Conclusions |
| Summary |
Complexity of Seismic Time Series: Measurement and Application applies the tools of nonlinear dynamics to seismic analysis, allowing for the revelation of new details in micro-seismicity, new perspectives in seismic noise, and new tools for prediction of seismic events. The book summarizes both advances and applications in the field, thus meeting the needs of both fundamental and practical seismology. Merging the needs of the classical field and the very modern terms of complexity science, this book covers theory and its application to advanced nonlinear time series tools to investigate Earth's vibrations, making it a valuable tool for seismologists, hazard managers and engineers |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed May 25, 2018) |
| Subject |
Seismology -- Statistical methods
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Time-series analysis.
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SCIENCE -- Earth Sciences -- Geography.
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SCIENCE -- Earth Sciences -- Geology.
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Seismology -- Statistical methods
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Time-series analysis
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| Form |
Electronic book
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| Author |
Chelidze, Tamaz, editor
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Vallianatos, Filippos, editor
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Telesca, Luciano, editor
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| ISBN |
9780128131398 |
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012813139X |
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