Summary
It is natural to use a branching process to describe occurrence patterns of earthquakes, which are apparently clustered in both space and time. The clustering features of earthquakes are important for seismological studies.
Based on some empirical laws in seismicity studies, several point-process models have been proposed in literature, classifying seismicity into two components, background seismicity and clustering seismicity, where each earthquake event, no matter it is a background event or generated by another event, produces (triggers) its own offspring (aftershocks) according to some branching rules. There are further ideas on probability separation of background seismicity from the clustering seismicity assuming a constant background occurrence rate throughout the whole studied region and other authors proposed a stochastic declustering method and made the probability based declustering method practical.
In this paper, we show some useful graphical diagnostic methods for improving model formulation.
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References
A.J. Baddeley, R. Turner, J. Möller and M. Hazelton. Residual analysis for spatial point processes. Technical report, School of Mathematics & Statistics, University of Western Australia, 2004.
P. Brémaud. Point Processess and Queues. Springer-Verlag, 1980.
R. Console and M. Murru. A simple and testable model for earthquake clustering. Journal of Geophysical Research, 106:8699–8711, 2001.
R. Console, M. Murru and A.M. Lombardi. Refining earthquake clustering models. Journal of Geophysical Research, 108(B10):doi:10.1029/2002JB002130, 2003.
D.J. Daley and D. Vere-Jones. An Introduction to the Theory of Point Processes, 2nd edition. Springer, New York, 2003.
Y.Y. Kagan. Likelihood analysis of earthquake catalogues. Journal of Geophysical Research, 106(B7):135–148, 1991.
Y.Y. Kagan and L. Knopoff. Statistical study of the occurrence of shallow earthquakes. Geophysical Journal of the Royal Astronomical Society, 1978.
P.A.W. Lewis and E. Shedler. Simulation of non-homogeneous poisson processes by thinning. Naval Research Logistics Quarterly, 26:403–413, 1979.
F. Musmeci and D. Vere-Jones. A space-time clustering model for historical earthquakes. Annals of the Institute of Statistical Mathematics, 44:1–11, 1992.
Y. Ogata. On lewis’ simulation method for point processes. IEEE translations on information theory, IT-27:23–31, 1981.
Y. Ogata. Statistical models for earthquake occurrences and residual analysis for point processes. Journal of American Statistical Association, 83:9–27, 1988.
Y. Ogata. Space-time point-process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics, 50:379–402, 1998.
Y. Ogata, K. Katsura and M. Tanemura. Modelling heterogeneous spacetime occurrences of earthquake and its residual analysis. Journal of the Royal Statistical Society: Applied Statistics, 52(4):499–509, 2003.
F. Omori. On after-shocks of earthquakes. Journal of the College of Science, Imperial University of Tokyo, 7:111–200, 1898.
S.L. Rathbun. Modeling marked spatio-temporal point patterns. Bulletin of the International Statistical Institute, 55(2):379–396, 1993.
F.P. Schoenberg. Multi-dimensional residual analysis of point process models for earthquake occurrences. Journal of the American Statistical Association, 98:789–795, 2004.
D. Stoyan and P. Grabarnik. Second-order characteristics for stochastic structures connected with gibbs point processes. Mathematische Nachritchten, 151:95–100, 1991.
T. Utsu. Aftershock and earthquake statistics (i): Some parameters which characterize an aftershock sequence and their interrelations. Journal of the Faculty of Science, Hokkaido University, 3, Ser. VII (Geophysics):129–195, 1969.
T. Utsu, Y. Ogata and R.S. Matsu’ura. The centenary of the omori formula for a decay law of aftershock activity. Journal of Physical Earth, 1995:1–33, 1995.
Y. Yamanaka and K. Shimazaki. Scaling relationship between the number of aftershocks and the size of the main shock. Journal of Physical Earth, 1990:305–324, 1990.
J. Zhuang, C.-P. Chang, Y. Ogata and Y.-I. Chen. A study on the background and clustering seismicity in the taiwan region by using a point process model. Journal of Geophysical Research, 110(B05S18):doi:10.1029/2004JB003157, 2005.
J. Zhuang, Y. Ogata and D. Vere-Jones. Stochastic declustering of spacetime earthquake occurrences. Journal of the American Statistical Association, 97:369–380, 2002.
J. Zhuang, Y. Ogata and D. Vere-Jones. Analyzing earthquake clustering features by using stochastic reconstruction. Journal of Geophysical Research, 109(No. B5):doi:10.1029/2003JB002879, 2004.
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Zhuang, J., Ogata, Y., Vere-Jones, D. (2006). Diagnostic Analysis of Space-Time Branching Processes for Earthquakes. In: Baddeley, A., Gregori, P., Mateu, J., Stoica, R., Stoyan, D. (eds) Case Studies in Spatial Point Process Modeling. Lecture Notes in Statistics, vol 185. Springer, New York, NY. https://doi.org/10.1007/0-387-31144-0_15
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DOI: https://doi.org/10.1007/0-387-31144-0_15
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