{"product_id":"statistical-methods-and-modeling-of-seismogenesis-hardback-9781789450378","title":"Statistical Methods and Modeling of Seismogenesis (Hardback) 9781789450378","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eStatistical Methods and Modeling of Seismogenesis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eNikolaos Limnios (Edited by), Nikolaos Limnios (Author), Eleftheria Papadimitriou (Edited by), Eleftheria Papadimitriou (Author), George Tsaklidis (Edited by), George Tsaklidis (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781789450378, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 22 June 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e336 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eThe study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. \u003c\/p\u003e\n\u003cp\u003eStochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003cbr\u003e\u003ci\u003eNikolaos LIMNIOS, Eleftheria PAPADIMITRIOU and George TSAKLIDIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Kernel Density Estimation in Seismology \u003c\/b\u003e\u003cb\u003e1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStanisław LASOCKI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.2. Complexity of magnitude distribution 7\u003c\/p\u003e \u003cp\u003e1.3. Kernel estimation of magnitude distribution 13\u003c\/p\u003e \u003cp\u003e1.4. Implications for hazard assessments 14\u003c\/p\u003e \u003cp\u003e1.5. Interval estimation of magnitude CDF and related hazard parameters 16\u003c\/p\u003e \u003cp\u003e1.6. Transformation to equivalent dimensions 19\u003c\/p\u003e \u003cp\u003e1.7. References 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Earthquake Simulators Development and Application \u003c\/b\u003e\u003cb\u003e27\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRodolfo CONSOLE, Roberto CARLUCCIO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 28\u003c\/p\u003e \u003cp\u003e2.2. Development of earthquake simulators in the seismological literature 28\u003c\/p\u003e \u003cp\u003e2.2.1. ALLCAL 28\u003c\/p\u003e \u003cp\u003e2.2.2. Virtual quake 29\u003c\/p\u003e \u003cp\u003e2.2.3. RSQSim 30\u003c\/p\u003e \u003cp\u003e2.2.4. ViscoSim 30\u003c\/p\u003e \u003cp\u003e2.2.5. Other simulation codes 30\u003c\/p\u003e \u003cp\u003e2.2.6. Comparisons among simulators 31\u003c\/p\u003e \u003cp\u003e2.3. Conceptual evolution of a physics-based earthquake simulator 32\u003c\/p\u003e \u003cp\u003e2.3.1. A physics-based earthquake simulator (2015) 33\u003c\/p\u003e \u003cp\u003e2.3.2. Frequency-magnitude distribution of the simulated catalog (2015) 36\u003c\/p\u003e \u003cp\u003e2.3.3. Temporal features of the synthetic catalog (2015) 38\u003c\/p\u003e \u003cp\u003e2.3.4. Improvements in the physics-based earthquake simulator (2017–2018) 41\u003c\/p\u003e \u003cp\u003e2.3.5. Application to the seismicity of Central Italy 42\u003c\/p\u003e \u003cp\u003e2.3.6. Further improvements of the simulator code (2019) 46\u003c\/p\u003e \u003cp\u003e2.4. Application of the last version of the simulator to the Nankai mega-thrust fault system 49\u003c\/p\u003e \u003cp\u003e2.5. Appendix 1: Relations among source parameters adopted in the simulation model 54\u003c\/p\u003e \u003cp\u003e2.6. Appendix 2: Outline of the simulation program 56\u003c\/p\u003e \u003cp\u003e2.7. References 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Statistical Laws of Post-seismic Activity \u003c\/b\u003e\u003cb\u003e63\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePeter SHEBALIN, Sergey BARANOV\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 63\u003c\/p\u003e \u003cp\u003e3.2. Earthquake productivity 64\u003c\/p\u003e \u003cp\u003e3.2.1. The proposed method to study productivity 65\u003c\/p\u003e \u003cp\u003e3.2.2. Earthquake productivity at the global level 69\u003c\/p\u003e \u003cp\u003e3.2.3. Independence of the proximity function 72\u003c\/p\u003e \u003cp\u003e3.2.4. Earthquake productivity at the regional level 76\u003c\/p\u003e \u003cp\u003e3.2.5. Productivity in relation to the threshold of the proximity function 78\u003c\/p\u003e \u003cp\u003e3.2.6. Discussion 79\u003c\/p\u003e \u003cp\u003e3.3. Time-dependent distribution of the largest aftershock magnitude 81\u003c\/p\u003e \u003cp\u003e3.3.1. The distribution of the magnitude of the largest aftershock in relation to time 82\u003c\/p\u003e \u003cp\u003e3.3.2. The agreement between the dynamic Båth law and observations 85\u003c\/p\u003e \u003cp\u003e3.3.3. Discussion 86\u003c\/p\u003e \u003cp\u003e3.4. The distribution of the hazardous period 88\u003c\/p\u003e \u003cp\u003e3.4.1. A model for the duration of the hazardous period 89\u003c\/p\u003e \u003cp\u003e3.4.2. Determining the model parameters 91\u003c\/p\u003e \u003cp\u003e3.4.3. Using the early aftershocks 96\u003c\/p\u003e \u003cp\u003e3.5. Conclusion 98\u003c\/p\u003e \u003cp\u003e3.6. References 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Explaining Foreshock and the Båth Law Using a Generic Earthquake Clustering Model \u003c\/b\u003e\u003cb\u003e105\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJiancang ZHUANG\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 105\u003c\/p\u003e \u003cp\u003e4.1.1. Issues related to foreshocks 106\u003c\/p\u003e \u003cp\u003e4.1.2. Issues related to the Båth law 108\u003c\/p\u003e \u003cp\u003e4.1.3. Study objectives 108\u003c\/p\u003e \u003cp\u003e4.2. Theories related to foreshock probability and the Båth law under the assumptions of the ETAS model 109\u003c\/p\u003e \u003cp\u003e4.2.1. Space–time ETAS model, stochastic declustering and classification of earthquakes 109\u003c\/p\u003e \u003cp\u003e4.2.2. Master equation 110\u003c\/p\u003e \u003cp\u003e4.2.3. Asymptotic property of F(m’) 113\u003c\/p\u003e \u003cp\u003e4.2.4. Foreshock probabilities and their magnitude distribution in the ETAS model 117\u003c\/p\u003e \u003cp\u003e4.2.5. Explanation of the Båth law by the ETAS model 118\u003c\/p\u003e \u003cp\u003e4.3. Foreshock simulations based on the ETAS model 120\u003c\/p\u003e \u003cp\u003e4.3.1. Works by Helmstetter and others 120\u003c\/p\u003e \u003cp\u003e4.3.2. Works by Zhuang and others 120\u003c\/p\u003e \u003cp\u003e4.3.3. Evidence of statistics between mainshocks and foreshocks 121\u003c\/p\u003e \u003cp\u003e4.3.4. Different simulation results 121\u003c\/p\u003e \u003cp\u003e4.4. Simulation of the Båth law based on the ETAS model 123\u003c\/p\u003e \u003cp\u003e4.4.1. On the simulation study by Helmstetter 123\u003c\/p\u003e \u003cp\u003e4.4.2. Observation on Båth’s law for volcanic earthquake swarms 124\u003c\/p\u003e \u003cp\u003e4.5. Conclusion 125\u003c\/p\u003e \u003cp\u003e4.5.1. Back to the starting point 125\u003c\/p\u003e \u003cp\u003e4.5.2. On the comparison between foreshock probability in the ETAS model and real catalogs 125\u003c\/p\u003e \u003cp\u003e4.5.3. Impracticality of the foreshock concept 126\u003c\/p\u003e \u003cp\u003e4.5.4. What should we do? 126\u003c\/p\u003e \u003cp\u003e4.6. Acknowledgments 127\u003c\/p\u003e \u003cp\u003e4.7. References 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. The Genesis of Aftershocks in Spring Slider Models \u003c\/b\u003e\u003cb\u003e131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEugenio LIPPIELLO, Giuseppe PETRILLO, François LANDES and Alberto ROSSO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 131\u003c\/p\u003e \u003cp\u003e5.2. The rate-and-state equation 133\u003c\/p\u003e \u003cp\u003e5.3. The Dieterich model 134\u003c\/p\u003e \u003cp\u003e5.3.1. Time to instability 135\u003c\/p\u003e \u003cp\u003e5.3.2. Initial conditions during stationary seismicity 137\u003c\/p\u003e \u003cp\u003e5.3.3. Effect of a constant stress increase Δτ 137\u003c\/p\u003e \u003cp\u003e5.4. The mechanics of afterslip 138\u003c\/p\u003e \u003cp\u003e5.5. The two-block model 140\u003c\/p\u003e \u003cp\u003e5.5.1. Synthetic catalogs 142\u003c\/p\u003e \u003cp\u003e5.6. Conclusion 146\u003c\/p\u003e \u003cp\u003e5.7. References 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Markov Regression Models for Time Series of Earthquake Counts \u003c\/b\u003e\u003cb\u003e153\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDimitris KARLIS, Katerina ORFANOGIANNAKI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 153\u003c\/p\u003e \u003cp\u003e6.2. Markov regression HMMs: definition and notation 156\u003c\/p\u003e \u003cp\u003e6.3. Application 157\u003c\/p\u003e \u003cp\u003e6.3.1. Data 157\u003c\/p\u003e \u003cp\u003e6.3.2. Results 160\u003c\/p\u003e \u003cp\u003e6.4. Conclusion 163\u003c\/p\u003e \u003cp\u003e6.5. Acknowledgments 166\u003c\/p\u003e \u003cp\u003e6.6. References 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Scaling Properties, Multifractality and Range of Correlations in Earthquake Time Series: Are Earthquakes Random? \u003c\/b\u003e\u003cb\u003e171\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGeorgios MICHAS, Filippos VALLIANATOS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 171\u003c\/p\u003e \u003cp\u003e7.2. The range of correlations in earthquake time series 173\u003c\/p\u003e \u003cp\u003e7.2.1. Short-range correlations 173\u003c\/p\u003e \u003cp\u003e7.2.2. Long-range correlations 177\u003c\/p\u003e \u003cp\u003e7.3. Scaling properties of earthquake time series 183\u003c\/p\u003e \u003cp\u003e7.3.1. The probability distribution function 184\u003c\/p\u003e \u003cp\u003e7.3.2. A stochastic dynamic mechanism with memory effects 192\u003c\/p\u003e \u003cp\u003e7.3.3. The cumulative distribution function 195\u003c\/p\u003e \u003cp\u003e7.4. Fractal and multifractal structures 197\u003c\/p\u003e \u003cp\u003e7.5. Discussion and conclusion 201\u003c\/p\u003e \u003cp\u003e7.6. References 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Self-correcting Models in Seismology: Possible Coupling Among Seismic Areas \u003c\/b\u003e\u003cb\u003e211\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOurania MANGIRA, Eleftheria PAPADIMITRIOU, Georgios VASILIADIS and George TSAKLIDIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 211\u003c\/p\u003e \u003cp\u003e8.2. Review of applications 212\u003c\/p\u003e \u003cp\u003e8.3. Formulation of the models 218\u003c\/p\u003e \u003cp\u003e8.3.1. Simple Stress Release Model 218\u003c\/p\u003e \u003cp\u003e8.3.2. Independent Stress Release Model 220\u003c\/p\u003e \u003cp\u003e8.3.3. Linked Stress Release Model 220\u003c\/p\u003e \u003cp\u003e8.4. Applications 222\u003c\/p\u003e \u003cp\u003e8.4.1. Greece and the surrounding area 222\u003c\/p\u003e \u003cp\u003e8.4.2. Gulf of Corinth 229\u003c\/p\u003e \u003cp\u003e8.5. Conclusion 235\u003c\/p\u003e \u003cp\u003e8.6. References 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Markovian Arrival Processes for Earthquake Clustering Analysis \u003c\/b\u003e\u003cb\u003e241\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePolyzois BOUNTZIS, Eleftheria PAPADIMITRIOU and George TSAKLIDIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 241\u003c\/p\u003e \u003cp\u003e9.2. State of the art 243\u003c\/p\u003e \u003cp\u003e9.2.1. Earthquake clustering methods and applications 243\u003c\/p\u003e \u003cp\u003e9.2.2. Hidden Markov models and applications in seismology 244\u003c\/p\u003e \u003cp\u003e9.3. Markovian Arrival Process 247\u003c\/p\u003e \u003cp\u003e9.3.1. Definition and basic results 248\u003c\/p\u003e \u003cp\u003e9.3.2. Parameter fitting 250\u003c\/p\u003e \u003cp\u003e9.3.3. Inference of the latent states 252\u003c\/p\u003e \u003cp\u003e9.4. Methodology and results 254\u003c\/p\u003e \u003cp\u003e9.4.1. Motivation 254\u003c\/p\u003e \u003cp\u003e9.4.2. Clustering detection procedure 254\u003c\/p\u003e \u003cp\u003e9.5. Conclusion 264\u003c\/p\u003e \u003cp\u003e9.6. References 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Change Point Detection Techniques on Seismicity Models \u003c\/b\u003e\u003cb\u003e271\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRodi LYKOU, George TSAKLIDIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 271\u003c\/p\u003e \u003cp\u003e10.2. The change point framework 272\u003c\/p\u003e \u003cp\u003e10.3. Changes in a Poisson process 276\u003c\/p\u003e \u003cp\u003e10.4. Changes in the Epidemic Type Aftershock Sequence model 279\u003c\/p\u003e \u003cp\u003e10.5. Changes in the Gutenberg–Richter law 282\u003c\/p\u003e \u003cp\u003e10.6. ZMAP 286\u003c\/p\u003e \u003cp\u003e10.7. Other statistical tests 287\u003c\/p\u003e \u003cp\u003e10.8. Detection of changes without hypothesis testing 289\u003c\/p\u003e \u003cp\u003e10.9. Discussion and conclusion 290\u003c\/p\u003e \u003cp\u003e10.10. References 291\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Semi-Markov Processes for Earthquake Forecast 299\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVlad Stefan BARBU, Alex KARAGRIGORIOU and Andreas MAKRIDES\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction 299\u003c\/p\u003e \u003cp\u003e11.2. Semi-Markov processes – preliminaries 300\u003c\/p\u003e \u003cp\u003e11.2.1. Special class of distributions 303\u003c\/p\u003e \u003cp\u003e11.3. Transition probabilities and earthquake occurrence 304\u003c\/p\u003e \u003cp\u003e11.3.1. Likelihood and estimation 304\u003c\/p\u003e \u003cp\u003e11.4. Semi-Markov transition matrix 305\u003c\/p\u003e \u003cp\u003e11.5. Illustrative example 307\u003c\/p\u003e \u003cp\u003e11.6. References 308\u003c\/p\u003e \u003cp\u003eList of Authors 309\u003c\/p\u003e \u003cp\u003eIndex 311\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52470029091096,"sku":"9781789450378","price":137.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781789450378.jpg?v=1785628877","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/statistical-methods-and-modeling-of-seismogenesis-hardback-9781789450378","provider":"Freshly Printed Books","version":"1.0","type":"link"}