Muutke küpsiste eelistusi

E-raamat: Earthquakes - Models, Statistics, Testable Forecasts: Models, Statistics, Testable Forecasts [Wiley Online]

  • Formaat: 306 pages
  • Sari: Wiley Works
  • Ilmumisaeg: 21-Feb-2014
  • Kirjastus: American Geophysical Union
  • ISBN-10: 1118637917
  • ISBN-13: 9781118637913
Teised raamatud teemal:
  • Wiley Online
  • Hind: 169,12 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Earthquakes - Models, Statistics, Testable Forecasts: Models, Statistics, Testable ForecastsSuurem pilt
  • Formaat: 306 pages
  • Sari: Wiley Works
  • Ilmumisaeg: 21-Feb-2014
  • Kirjastus: American Geophysical Union
  • ISBN-10: 1118637917
  • ISBN-13: 9781118637913
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118637913
"Our purpose is to analyze the causes of recent failures in earthquake forecasting, as well as the difficulties in earthquake investigation"--

"The proposed book is the first comprehensive and methodologically rigorous analysis of earthquake occurrence. Models based on the theory of the stochastic multidimensional point processes are employed to approximate the earthquake occurrence pattern andevaluate its parameters. The Author shows that most of these parameters have universal values. These results help explain the classical earthquake distributions: Omori's law and the Gutenberg-Richter relation. The Author derives a new negative-binomial distribution for earthquake numbers, instead of the Poisson distribution, and then determines a fractal correlation dimension for spatial distributions of earthquake hypocenters. The book also investigates the disorientation of earthquake focal mechanismsand shows that it follows the rotational Cauchy distribution. These statistical and mathematical advances make it possible to produce quantitative forecasts of earthquake occurrence. In these forecasts earthquake rate in time, space, and focal mechanism orientation is evaluated"--

This book is the first comprehensive and methodologically rigorous analysis of earthquake occurrence. Models based on the theory of the stochastic multidimensional point processes are employed to approximate the earthquake occurrence pattern and evaluate its parameters. The Author shows that most of these parameters have universal values. These results help explain the classical earthquake distributions: Omori's law and the Gutenberg-Richter relation.

The Author derives a new negative-binomial distribution for earthquake numbers, instead of the Poisson distribution, and then determines a fractal correlation dimension for spatial distributions of earthquake hypocenters. The book also investigates the disorientation of earthquake focal mechanisms and shows that it follows the rotational Cauchy distribution. These statistical and mathematical advances make it possible to produce quantitative forecasts of earthquake occurrence. In these forecasts earthquake rate in time, space, and focal mechanism orientation is evaluated.

Preface xiii
Acknowledgments xvii
List of Abbreviations xix
List of Mathematical Symbols xxi
Part I Models 1(30)
1 Motivation: Earthquake science challenges
3(3)
2 Seismological background
6(15)
2.1 Earthquakes
6(2)
2.2 Earthquake catalogs
8(3)
2.3 Description of modern earthquake catalogs
11(3)
2.4 Earthquake temporal occurrence: quasi-periodic, Poisson, or clustered?
14(2)
2.5 Earthquake faults: one fault, several faults, or an infinite number of faults?
16(2)
2.6 Statistical and physical models of seismicity
18(1)
2.7 Laboratory and theoretical studies of fracture
19(2)
3 Stochastic processes and earthquake occurrence models
21(10)
3.1 Earthquake clustering and branching processes
21(3)
3.2 Several problems and challenges
24(2)
3.3 Critical continuum-state branching model of earthquake rupture
26(7)
3.3.1 Time-magnitude simulation
26(2)
3.3.2 Space-focal mechanism simulation
28(3)
Part II Statistics 31(152)
4 Statistical distributions of earthquake numbers: Consequence of branching process
33(21)
4.1 Theoretical considerations
34(9)
4.1.1 Generating function for the negative binomial distribution (NBD)
34(5)
4.1.2 NBD distribution expressions
39(2)
4.1.3 Statistical parameter estimation
41(2)
4.2 Observed earthquake numbers distribution
43(11)
4.2.1 Statistical analysis of earthquake catalogs
43(1)
4.2.2 Observed earthquake numbers distributions
43(3)
4.2.3 Likelihood analysis
46(3)
4.2.4 Tables of parameters
49(5)
5 Earthquake size distribution
54(42)
5.1 Magnitude versus seismic moment
54(2)
5.2 Seismic moment distribution
56(4)
5.3 Is β identical to 1/2
60(20)
5.3.1 Preamble
60(2)
5.3.2 Catalog analysis and earthquake size distribution
62(1)
5.3.3 Systematic and random effects in determining earthquake size
63(14)
5.3.4 Dislocation avalanche statistics
77(2)
5.3.5 What are β identical to 1/2 consequences?
79(1)
5.4 Seismic moment sum distribution
80(6)
5.4.1 Simulation and analytical results
80(3)
5.4.2 Applications to seismicity analysis
83(3)
5.5 Length of aftershock zone (earthquake spatial scaling)
86(4)
5.6 Maximum or corner magnitude: 2004 Sumatra and 2011 Tohoku mega-earthquakes
90(6)
5.6.1 Maximum moment for subduction zones
90(1)
5.6.2 Seismic moment conservation principle
90(6)
6 Temporal earthquake distribution
96(29)
6.1 Omori's law
96(1)
6.2 Seismic moment release in earthquakes and aftershocks
97(10)
6.2.1 Temporal distribution of aftershocks
97(2)
6.2.2 Southern California earthquakes and their aftershocks
99(4)
6.2.3 Global shallow earthquakes
103(2)
6.2.4 Comparison of source-time functions and aftershock moment release
105(2)
6.3 Random shear stress and Omori's law
107(3)
6.4 Aftershock temporal distribution, theoretical analysis
110(6)
6.4.1 Levy distribution
110(2)
6.4.2 Inverse Gaussian distribution (IGD)
112(4)
6.5 Temporal distribution of aftershocks: Observations
116(5)
6.5.1 Aftershock sequences
116(1)
6.5.2 Temporal distribution for earthquake pairs
116(5)
6.6 Example: The New Madrid earthquake sequence of 1811-12
121(2)
6.7 Conclusion
123(2)
7 Earthquake location distribution
125(21)
7.1 Multipoint spatial statistical moments
125(2)
7.2 Sources of error and bias in estimating the correlation dimension
127(14)
7.2.1 The number of earthquakes in a sample
128(1)
7.2.2 Earthquake location error
128(4)
7.2.3 Projection effect for epicentral scaling dimension
132(2)
7.2.4 Boundary effects
134(2)
7.2.5 Inhomogeneity of earthquake depth distribution
136(2)
7.2.6 Temporal influence
138(3)
7.2.7 Randomness
141(1)
7.3 Correlation dimension for earthquake catalogs
141(4)
7.3.1 California catalogs
141(3)
7.3.2 Global PDE catalog
144(1)
7.4 Conclusion
145(1)
8 Focal mechanism orientation and source complexity
146(37)
8.1 Random stress tensor and seismic moment tensor
147(3)
8.1.1 Challenges in stress studies
147(1)
8.1.2 Cauchy stress distribution
148(1)
8.1.3 Random stress tensors
149(1)
8.2 Geometric complexity of earthquake focal zone and fault systems
150(4)
8.2.1 Tensor invariants
150(2)
8.2.2 CLOD sources and complexity
152(2)
8.3 Rotation of double-couple (DC) earthquake moment tensor and quaternions
154(5)
8.3.1 Quaternions
154(2)
8.3.2 DC moment tensor and quaternions
156(3)
8.4 Focal mechanism symmetry
159(4)
8.4.1 Symmetry of DC source
160(2)
8.4.2 DC symmetry and rotation angle
162(1)
8.5 Earthquake focal mechanism and crystallographic texture statistics
163(4)
8.6 Rotation angle distributions
167(3)
8.6.1 Uniform random rotation of DC sources
167(1)
8.6.2 Non-uniform distributions of random rotations
168(2)
8.7 Focal mechanisms statistics
170(7)
8.7.1 Disorientation angle statistics
170(3)
8.7.2 Distributions of rotation axes
173(1)
8.7.3 Rodrigues space statistics and display
174(2)
8.7.4 Summary of results for DC orientation
176(1)
8.8 Models for complex earthquake sources
177(8)
8.8.1 Complex point source solutions
177(2)
8.8.2 Higher-rank correlation tensors
179(4)
Part III Testable Forecasts 183(77)
9 Global earthquake patterns
185(21)
9.1 Earthquake time-space patterns
185(2)
9.2 Defining global tectonic zones
187(1)
9.3 Corner magnitudes in the tectonic zones
188(2)
9.4 Critical branching model (CBM) of earthquake occurrence
190(7)
9.4.1 Branching models
190(2)
9.4.2 Earthquake clusters - independent events
192(1)
9.4.3 Dependent events
192(3)
9.4.4 Stochastic branching processes and temporal dependence
195(2)
9.5 Likelihood analysis of catalogs
197(7)
9.5.1 Statistical analysis results
197(6)
9.5.2 Comparison of results with the ETAS model
203(1)
9.6 Results of the catalogs' statistical analysis
204(2)
10 Long- and short-term earthquake forecasting
206(23)
10.1 Phenomenological branching models and earthquake occurrence estimation
206(1)
10.2 Long-term rate density estimates
207(8)
10.2.1 Low-resolution forecasts
207(3)
10.2.2 High-resolution global forecasts
210(1)
10.2.3 Smoothing kernel selection
210(3)
10.2.4 Comparing long-term forecasts
213(2)
10.3 Short-term forecasts
215(3)
10.4 Example: earthquake forecasts during the Tohoku sequence
218(6)
10.4.1 Long- and short-term earthquake forecasts during the Tohoku sequence
218(3)
10.4.2 Long-term earthquake rates for the Tokyo region
221(3)
10.5 Forecast results and their discussion
224(2)
10.6 Earthquake fault propagation modeling and earthquake rate estimation
226(3)
10.6.1 Earthquake extended rupture representation and earthquake rate estimation
227(1)
10.6.2 Earthquake fault propagation modeling
227(2)
11 Testing long-term earthquake forecasts: Likelihood methods and error diagrams
229(24)
11.1 Preamble
229(1)
11.2 Log-likelihood and information score
230(5)
11.3 Error diagram (ED)
235(12)
11.3.1 Relation between the error diagram and information score
237(6)
11.3.2 Two-segment error diagrams and information score
243(2)
11.3.3 Information score for GCMT and PDE catalogs
245(2)
11.4 Tests and optimization for global high-resolution forecasts
247(3)
11.5 Summary of testing results
250(3)
12 Future prospects and problems
253(7)
12.1 Community efforts for statistical seismicity analysis and earthquake forecast testing
253(1)
12.1.1 Community Online Resource for Statistical Seismicity Analysis (CORSSA)
253(1)
12.1.2 Collaboratory for the Study of Earthquake Predictability (CSEP): Global and regional forecast testing
254(1)
12.2 Results and challenges
254(2)
12.3 Future developments
256(4)
References 260(21)
Index 281
Yan Kagan grew up and was educated in Moscow, Russia. In 1974 he came to UCLA, and working with Leon Knopoff, David Jackson, Peter Bird, and Frederick Schoenberg applied his mathematical/statistical model to seismicity analysis. Since 1999 these results have been used to produce daily earthquake forecasts for several seismically active regions and currently for the whole Earth. The performance and predictive skill of these forecasts is now being tested by several research groups.
Püsilink: https://www.kriso.ee/db/9781118637913_pe.html
Märksõnad: