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    Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

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    Type
    Thesis
    Authors
    Sawlan, Zaid A cc
    Advisors
    Hoteit, Ibrahim cc
    Committee Members
    Laleg-Kirati, Taous-Meriem cc
    Scavino, Marco cc
    Program
    Applied Mathematics and Computational Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2012-12
    Permanent link to this record
    http://hdl.handle.net/10754/255453
    
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    Abstract
    Tsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.
    DOI
    10.25781/KAUST-92RB8
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-92RB8
    Scopus Count
    Collections
    Applied Mathematics and Computational Science Program; Theses; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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