Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea

Handle URI:
http://hdl.handle.net/10754/255453
Title:
Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea
Authors:
Sawlan, Zaid A
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.
Advisors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Committee Member:
Laleg-Kirati, Taous-Meriem ( 0000-0001-5944-0121 ) ; Scavino, Marco
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Applied Mathematics and Computational Science
Issue Date:
Dec-2012
Type:
Thesis
Appears in Collections:
Applied Mathematics and Computational Science Program; Theses; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.advisorHoteit, Ibrahimen
dc.contributor.authorSawlan, Zaid Aen
dc.date.accessioned2012-12-12T06:46:11Z-
dc.date.available2012-12-12T06:46:11Z-
dc.date.issued2012-12en
dc.identifier.urihttp://hdl.handle.net/10754/255453en
dc.description.abstractTsunami 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.en
dc.language.isoenen
dc.subjectTsunami Prediction and Earthquakeen
dc.subjectKalman Filter and Kalman Smootheren
dc.subjectTsunami Predictions in the Red Seaen
dc.subjectData Assimilationen
dc.subjectParameter Estimationen
dc.subjectEnsemble Kalman Filteren
dc.titleTsunami Prediction and Earthquake Parameters Estimation in the Red Seaen
dc.typeThesisen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberLaleg-Kirati, Taous-Meriemen
dc.contributor.committeememberScavino, Marcoen
thesis.degree.disciplineApplied Mathematics and Computational Scienceen
thesis.degree.nameMaster of Scienceen
dc.person.id113176en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.