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Pure and Applied Geophysics

, Volume 174, Issue 6, pp 2381–2399 | Cite as

An Ensemble Approach for Improved Short-to-Intermediate-Term Seismic Potential Evaluation

  • Huaizhong Yu
  • Qingyong Zhu
  • Faren Zhou
  • Lei Tian
  • Yongxian Zhang
Article
  • 126 Downloads

Abstract

Pattern informatics (PI), load/unload response ratio (LURR), state vector (SV), and accelerating moment release (AMR) are four previously unrelated subjects, which are sensitive, in varying ways, to the earthquake’s source. Previous studies have indicated that the spatial extent of the stress perturbation caused by an earthquake scales with the moment of the event, allowing us to combine these methods for seismic hazard evaluation. The long-range earthquake forecasting method PI is applied to search for the seismic hotspots and identify the areas where large earthquake could be expected. And the LURR and SV methods are adopted to assess short-to-intermediate-term seismic potential in each of the critical regions derived from the PI hotspots, while the AMR method is used to provide us with asymptotic estimates of time and magnitude of the potential earthquakes. This new approach, by combining the LURR, SV and AMR methods with the choice of identified area of PI hotspots, is devised to augment current techniques for seismic hazard estimation. Using the approach, we tested the strong earthquakes occurred in Yunnan–Sichuan region, China between January 1, 2013 and December 31, 2014. We found that most of the large earthquakes, especially the earthquakes with magnitude greater than 6.0 occurred in the seismic hazard regions predicted. Similar results have been obtained in the prediction of annual earthquake tendency in Chinese mainland in 2014 and 2015. The studies evidenced that the ensemble approach could be a useful tool to detect short-to-intermediate-term precursory information of future large earthquakes.

Keywords

Multi-method combination Yunnan–Sichuan region Chinese mainland seismic hazard evaluation 

Notes

Acknowledgments

The research was supported by the Spark Program of Earthquake Science of China (Grant No. XH12058) and the Grant support from the Chinese NSFC (No. 91230114).

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Copyright information

© Springer International Publishing 2016

Authors and Affiliations

  • Huaizhong Yu
    • 1
  • Qingyong Zhu
    • 2
  • Faren Zhou
    • 1
  • Lei Tian
    • 1
  • Yongxian Zhang
    • 1
  1. 1.Department of Earthquake PredictionChina Earthquake Networks CenterBeijingChina
  2. 2.School of EngineeringSun Yat-sen UniversityGuangzhouChina

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