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| Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes | |
| 作者姓名： | 王丽凤 马丽 陈时军 |
| 作者单位： | Center for Analysis and Prediction，China Earthquake Administration，Beijing 100036，China，Center for Analysis and Prediction，China Earthquake Administration，Beijing 100036，China DavidVere-JonesVictoria University of Wellington，Wellington P. O. Box 600，New Zealand，Earthquake Administration of Shandong Province，Jinan 250014，China |
| 基金项目： | National Natural Science Foundation of China (40074013,40134010),Chinese Joint Seismological Science Foundation (042002) and the project during the Tenth Five-year Plan. |
| 摘 要： | Introduction According to many published papers, seismicity in time-space domain shows some characteristics, such as doughnut epicenter distribution (Mogi, 1969) or quiescence of seismic activity before large earthquakes (WANG, et al, 2002), and aftershock decay (Ogata, 1998). In recent years, more and more seismologists (Lynnr, Steven, 1990) have found that many strong earthquakes are preceded by enhancing regional seismicity and accelerating strain energy release (ZHANG, et al, 2001). T… |
| 收稿时间： | 11 November 2002 |
| 修稿时间： | 29 July 2003 |
Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes |
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| Wangli Feng, Ma Li, David Vere-Jones and Chen Shi-jun.Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes[J].Acta Seismologica Sinica(English Edition),2004,17(2):177-189. | |
| Authors: | Wangli Feng Ma Li David Vere-Jones and Chen Shi-jun |
| Institution: | 1. Center for Analysis and Prediction, China Earthquake Administration, Beijing 100036, China 2. Victoria University of Wellington, Wellington P.O. Box 600, New Zealand 3. Earthquake Administration of Shandong Province, Ji'nan 250014, China |
| Abstract: | Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of several strong earthquakes in China and New Zealand. Akaikes AIC criterion is used to discriminate whether an accelerating mode of earthquake activity precedes those events or not. Finally, regional accelerating seismic activity and possible prediction approach for future strong earthquakes are discussed. |
| Keywords: | stochastic AMR model parameter estimation maximum likelihood AIC criterion strong earthquake |
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