Space-time point-process models for earthquake occurrences

被引:847
|
作者
Ogata, Y [1 ]
机构
[1] Inst Stat Math, Minato Ku, Tokyo 1068569, Japan
关键词
centroid of aftershock epicenters; ETAS model; inverse power laws; maximum likelihood estimates; magnitude based clustering (MBC) algorithm; modified Omori formula; thinning simulation;
D O I
10.1023/A:1003403601725
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Several space-time statistical models are constructed based on both classical empirical studies of clustering and some more speculative hypotheses. Then we discuss the discrimination between models incorporating contrasting assumptions concerning the form of the space-time clusters. We also examine further practical extensions of the model to situations where the background seismicity is spatially non-homogeneous, and the clusters are nonisotropic. The goodness-of-fit of the models, as measured by AIC values: is discussed for two high quality data sets, in different tectonic regions. AIC also allows the details of the clustering structure in space to be clarified. A simulation algorithm for the models is provided, and used to confirm the numerical accuracy of the likelihood calculations. The simulated data sets show the similar spatial distributions to the real ones, but differ from them in some features of space-time clustering. These differences may provide useful indicators of directions for further study.
引用
收藏
页码:379 / 402
页数:24
相关论文
共 50 条
  • [21] A point-process analysis of the Matsushiro earthquake swarm sequence: The effect of water on earthquake occurrence
    Matsu'ura, RS
    Karakama, I
    PURE AND APPLIED GEOPHYSICS, 2005, 162 (6-7) : 1319 - 1345
  • [22] SCORE-MATCHING ESTIMATORS FOR CONTINUOUS-TIME POINT-PROCESS REGRESSION MODELS
    Sahani, Maneesh
    Bohner, Gergo
    Meyer, Arne
    2016 IEEE 26TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2016,
  • [23] Prediction and validation of short-to-long-term earthquake probabilities in inland Japan using the hierarchical space-time ETAS and space-time Poisson process models
    Ogata, Yosihiko
    EARTH PLANETS AND SPACE, 2022, 74 (01):
  • [24] MODELS OF SPACE-TIME
    EBERLEIN, WF
    BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETY, 1965, 71 (05) : 731 - &
  • [25] Including covariates in a space-time point process with application to seismicity
    Adelfio, Giada
    Chiodi, Marcello
    STATISTICAL METHODS AND APPLICATIONS, 2021, 30 (03): : 947 - 971
  • [26] Including covariates in a space-time point process with application to seismicity
    Giada Adelfio
    Marcello Chiodi
    Statistical Methods & Applications, 2021, 30 : 947 - 971
  • [27] A STOCHASTIC SPACE-TIME MODEL FOR INTERMITTENT PRECIPITATION OCCURRENCES
    Sun, Ying
    Stein, Michael L.
    ANNALS OF APPLIED STATISTICS, 2015, 9 (04): : 2110 - 2132
  • [28] Self-exciting point process in modeling earthquake occurrences
    Pratiwi, H.
    Slamet, I.
    Saputro, D. R. S.
    Respatiwulan
    INTERNATIONAL CONFERENCE ON MATHEMATICS: EDUCATION, THEORY AND APPLICATION, 2017, 855
  • [29] Assessment of Gait Nonlinear Dynamics by Inhomogeneous Point-Process Models
    Valenza, Gaetano
    Citi, Luca
    Barbieri, Riccardo
    2014 36TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2014, : 6973 - 6976
  • [30] Combining point-process and landscape vegetation models to predict large herbivore distributions in space and time-A case study of Rupicapra rupicapra
    Thuiller, Wilfried
    Gueguen, Maya
    Bison, Marjorie
    Duparc, Antoine
    Garel, Mathieu
    Loison, Anne
    Renaud, Julien
    Poggiato, Giovanni
    DIVERSITY AND DISTRIBUTIONS, 2018, 24 (03) : 352 - 362