The Generalized Extreme Value Model for Seismic Hazard Estimation of Potential Source Area based on Maximum Information Entropy Principle
Luchuan Ren, Zhe Liu, Jianwei Tian
Available Online November 2016.
- https://doi.org/10.2991/rac-16.2016.10How to use a DOI?
- maximum information entropy principle; generalized extreme value model; potential seismic source area; seismic hazard estimation
- The author proposes a method to establish the generalized extreme value model for strong earthquake hazard estimation of a potential source area, based on the maximum information entropy principle and corresponding constraint conditions, and then selects the Ryukyu trench subduction zone as a potential seismic source area for case study. The results show: Considering that the Weibull distribution, among the three kinds of generalized extreme value distribution, has limited upper extreme point, and the upper bound magnitude of a potential seismic source area should be limited, therefore the Weibull distribution model should be selected to establish the generalized extreme value model for strong earthquake hazard estimation; Under the condition that the upper bound magnitude of a potential seismic source area be limited, and in according with the maximum information entropy principle, the probability density function of the maximum magnitude of a potential seismic source area, in each time interval, can be expressed as a general formula with three parameters, and these parameters can be calculated from the estimated values of shape parameter, location parameter and scale parameter of the corresponding Weibull distribution.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Luchuan Ren AU - Zhe Liu AU - Jianwei Tian PY - 2016/11 DA - 2016/11 TI - The Generalized Extreme Value Model for Seismic Hazard Estimation of Potential Source Area based on Maximum Information Entropy Principle PB - Atlantis Press SP - 59 EP - 64 SN - 1951-6851 UR - https://doi.org/10.2991/rac-16.2016.10 DO - https://doi.org/10.2991/rac-16.2016.10 ID - Ren2016/11 ER -