Pi Forecast with or Without De-clustering: an Experiment for the Sichuan-yunnan Region : Volume 11, Issue 3 (07/03/2011)

By Jiang, C. S.

Book Id: WPLBN0004017765
Format Type: PDF Article :
File Size: Pages 10
Reproduction Date: 2015

Title:	Pi Forecast with or Without De-clustering: an Experiment for the Sichuan-yunnan Region : Volume 11, Issue 3 (07/03/2011)
Author:	Jiang, C. S.
Volume:	Vol. 11, Issue 3
Language:	English
Subject:	Science, Natural, Hazards
Collections:	Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic Publication Date:	2011
Publisher:	Copernicus Gmbh, Göttingen, Germany

Member Page:	Copernicus Publications
Citation APA MLA Chicago Jiang, C. S., & Wu, Z. L. (2011). Pi Forecast with or Without De-clustering: an Experiment for the Sichuan-yunnan Region : Volume 11, Issue 3 (07/03/2011). Retrieved from http://gutenberg.cc/

Description
Description: Institute of Geophysics, China Earthquake Administration, Beijing 100081, China. Pattern Informatics (PI) algorithm uses earthquake catalogues for estimating the increase of the probability of strong earthquakes. The main measure in the algorithm is the number of earthquakes above a threshold magnitude. Since aftershocks occupy a significant proportion of the total number of earthquakes, whether de-clustering affects the performance of the forecast is one of the concerns in the application of this algorithm. This problem is of special interest after a great earthquake, when aftershocks become predominant in regional seismic activity. To investigate this problem, the PI forecasts are systematically analyzed for the Sichuan-Yunnan region of southwest China. In this region there have occurred some earthquakes larger than M_S 7.0, including the 2008 Wenchuan earthquake. In the analysis, the epidemic-type aftershock sequences (ETAS) model was used for de-clustering. The PI algorithm was revised to consider de-clustering, by replacing the number of earthquakes by the sum of the ETAS-assessed probability for an event to be a background event or a clustering event. Case studies indicate that when an intense aftershock sequence is included in the sliding time window, the hotspot picture may vary, and the variation lasts for about one year. PI forecasts seem to be affected by the aftershock sequence included in the anomaly identifying window, and the PI forecast using background events seems to have a better performance.

Summary
PI forecast with or without de-clustering: an experiment for the Sichuan-Yunnan region

Excerpt
Chen, C. C., Rundle, J. B., Holliday, J. R., Nanjo, K. Z., Turcotte, D. L., Li, S. C., and Tiampo, K. F.: The 1999 Chi-Chi, Taiwan, earthquake as a typical example of seismic activation and quiescence, Geophys. Res. Lett., 32, L22315, doi:10.1029/2005GL023991, 2005.; Gardner, J. K. and Knopoff, L.: Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian?, Bull. Seism. Soc. Amer., 64, 1363–1367, 1974.; Helmstetter, A. and Sornette, D.: Subcritical and supercritical regimes in epidemic models of earthquake aftershocks, J. Geophys. Res., 107, 2237, doi:10.1029/2001JB001580, 2002.; Helmstetter, A. and Sornette, D.: Foreshocks explained by cascades of triggered seismicity, J. Geophys. Res., 108, 2457, doi:10.1029/2003JB002409, 2003.; Holliday, J. R., Nanjo, K. Z., Tiampo, K. F., Rundle, J. B., and Turcotte, D. L.: Earthquake forecasting and its verification, Nonlin. Processes Geophys., 12, 965–977, doi:10.5194/npg-12-965-2005, 2005.; Holliday, J. R., Rundle, J. B., Tiampo, K. F., Klein, W., and Donnellan, A.: Modification of the pattern informatics method for forecasting large earthquake events using complex eigenfactors, Tectonophysics, 413, 87–91, 2006.; Holliday, J. R., Chen, C. C., Tiampo, K. F., Rundle, J. B., and Turcotte, D. L.: A RELM earthquake forecast based on pattern informatics, Seism. Res. Lett., 78, 87–93, 2007.; Swets, J. A.: The relative operating characteristic in psychology, Science, 182, 990–1000, 1973.; Jiang, C. S. and Wu, Z. L.: Retrospective forecasting test of a statistical physics model for earthquakes in Sichuan-Yunnan region, Science in China, Series D – Earth Sciences, 51, 1401–1410, 2008.; Jiang, C. S. and Wu, Z. L.: PI forecast for the Sichuan-Yunnan region: retrospective test after the May 12, 2008, Wechuan Earthquake, Pure Appl. Geophys., 167, 751–761, 2010.; Jiang, C. S. and Zhuang, J. C.: Evaluation of background seismicity and potential source zones of strong earthquakes in the Sichuan-Yunan region by using the space-time ETAS model, Chinese J. Geophys., 53(2), 305–317, doi:10.3969/j.issn.0001-5733.2010.02.001, 2010 (in Chinese with English abstract).; Keilis-Borok, V. I. and Rotwain, I. M.: Diagnosis of time of increased probability of strong earthquakes in different regions of the world, Phys. Earth Planet Interi., 61, 57–72, 1990.; Molchan, G. M.: Earthquake prediction as a decision-making problem, Pure Appl. Geophys., 149, 233–247, 1997.; Nanjo, K. Z., Rundle, J. B., Holliday, J. R., and Turcotte, D. L.: Pattern informatics and its application for optimal forecasting of large earthquakes in Japan, Pure Appl. Geophys., 163, 2417–2432, 2006.; Ogata, Y.: Statistical models for earthquake occurrences and residual analysis for point processes, J. Amer. Statist. Assoc., 83, 9–27, 1988.; Ogata, Y.: Seismicity and geodetic anomalies in a wide area preceding the Niigata-Ken-Chuetsu earthquake of 23 October 2004, central Japan, J. Geophys. Res., 112, B10301, doi:10.1029/2006JB004697, 2007.; Omori, F.: On aftershocks of earthquakes, J. Coll. Sci. Imp. Univ. Tokyo, 7, 11–200, 1894.; Rundle, J. B., Klein, W., Turcotte, D. L., and Malamud, B. D.: Precursory seismic activation and critical-point phenomena, Pure Appl. Geophys., 157, 2165–2182, 2000.; Rundle, J. B., Tiampo, K. F., Klein, W., and Matins, J. S. A.: Self-organization in leaky threshold systems: The influence of near mean field dynamics and its implications for earthquakes, neurobiology, and forecasting, Proc. Natl. Acad. Sci. USA, 99, 2514–2521, 2002.; Rundle, J. B., Turcotte, D. L., Shcherbakov, R., Klein, W., and Samm