Forward and Inverse Modeling of Tsunami Sediment Transport
| dc.contributor.author | Tang, Hui | en |
| dc.contributor.committeechair | Weiss, Robert | en |
| dc.contributor.committeemember | Romans, Brian W. | en |
| dc.contributor.committeemember | Eriksson, Kenneth A. | en |
| dc.contributor.committeemember | Irish, Jennifer L. | en |
| dc.contributor.department | Geosciences | en |
| dc.date.accessioned | 2017-04-22T08:00:56Z | en |
| dc.date.available | 2017-04-22T08:00:56Z | en |
| dc.date.issued | 2017-04-21 | en |
| dc.description.abstract | Tsunami is one of the most dangerous natural hazards in the coastal zone worldwide. Large tsunamis are relatively infrequent. Deposits are the only concrete evidence in the geological record with which we can determine both tsunami frequency and magnitude. Numerical modeling of sediment transport during a tsunami is important interdisciplinary research to estimate the frequency and magnitude of past events and quantitative prediction of future events. The goal of this dissertation is to develop robust, accurate, and computationally efficient models for sediment transport during a tsunami. There are two different modeling approaches (forward and inverse) to investigate sediment transport. A forward model consists of tsunami source, hydrodynamics, and sediment transport model. In this dissertation, we present one state-of-the-art forward model for Sediment TRansport In Coastal Hazard Events (STRICHE), which couples with GeoClaw and is referred to as GeoClaw-STRICHE. In an inverse model, deposit characteristics, such as grain-size distribution and thickness, are inputs to the model, and flow characteristics are outputs. We also depict one trial-and-error inverse model (TSUFLIND) and one data assimilation inverse model (TSUFLIND-EnKF) in this dissertation. All three models were validated and verified against several theoretical, experimental, and field cases. | en |
| dc.description.abstractgeneral | Population living close to coastlines is increasing, which creates higher risks due to coastal hazards, such as tsunami. Tsunamis are a series of long waves triggered by earthquakes, volcanic eruptions, landslides, and meteorite impacts. Deposits are the only concrete evidence in geological records that can be used to determine both tsunami frequency and magnitude. The numerical modeling of sediment transport in coastal hazard events is an important interdisciplinary research area to estimate the magnitude their magnitude. The goal of this dissertation is to develop several robust, accurate, and computationally efficient forward and inverse models for tsunami sediment transport. In Chapter one, a general literature review is given. Chapter two will discuss a new model for TSUunami FLow INversion from Deposits (TSUFLIND). TSUFLIND incorporates three models and adds new modules to simulate tsunami deposit formation and calculate flow condition. In Chapter three, we present an inverse model based on ensemble Kalman filtering (TSUFLIND-EnKF) to infer tsunami characteristics from deposits. This model is the first model that forms a system state to include both observable variables and unknown parameters. In Chapter four, we present a new forward model for simulating Sediment TRansport in Coastal Hazard Events, which combines with GeoClaw (GeoClaw-STRICHE). In Chapter five, we discuss the future works for TSUFLIND, TSUFLIND-EnKF, GeoClaw-STRICHE and forward-inverse framework. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:9875 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/77439 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Tsunami | en |
| dc.subject | Sediment Transport | en |
| dc.subject | Forward Model | en |
| dc.subject | Inverse Model | en |
| dc.title | Forward and Inverse Modeling of Tsunami Sediment Transport | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Geosciences | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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