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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.

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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

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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://cn.ebooklibrary.org/


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 MS 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
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