Baglione, Enrico
  
(2020)
Analysis of slip distribution of large earthquakes oriented to tsunamigenesis characterisation, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. 
 Dottorato di ricerca in 
Geofisica, 32 Ciclo. DOI 10.48676/unibo/amsdottorato/9252.
  
 
  
  
        
        
        
  
  
  
  
  
  
  
    
  
    
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      Abstract
      The present thesis focuses on the on-fault slip distribution of large earthquakes in the framework of tsunami hazard assessment and tsunami warning improvement. It is widely known that ruptures on seismic faults are strongly heterogeneous. In the case of tsunamigenic earthquakes, the slip heterogeneity strongly influences the spatial distribution of the largest tsunami effects along the nearest coastlines. Unfortunately, after an earthquake occurs, the so-called finite-fault models (FFM) describing the coseismic on-fault slip pattern becomes available over time scales that are incompatible with early tsunami warning purposes, especially in the near field. 
Our work aims to characterize the slip heterogeneity in a fast, but still suitable way.
Using finite-fault models to build a starting dataset of seismic events, the characteristics of the fault planes are studied with respect to the magnitude. The patterns of the slip distribution on the rupture plane, analysed with a cluster identification algorithm, reveal a preferential single-asperity representation that can be approximated by a two-dimensional Gaussian slip distribution (2D GD). The goodness of the 2D GD model is compared to other distributions used in literature and its ability to represent the slip heterogeneity in the form of the main asperity is proven. The magnitude dependence of the 2D GD parameters is investigated and turns out to be of primary importance from an early warning perspective.
The Gaussian model is applied to the 16 September 2015 Illapel, Chile, earthquake and used to compute early tsunami predictions that are satisfactorily compared with the available observations. The fast computation of the 2D GD and its suitability in representing the slip complexity of the seismic source make it a useful tool for the tsunami early warning assessments, especially for what concerns the near field.
     
    
      Abstract
      The present thesis focuses on the on-fault slip distribution of large earthquakes in the framework of tsunami hazard assessment and tsunami warning improvement. It is widely known that ruptures on seismic faults are strongly heterogeneous. In the case of tsunamigenic earthquakes, the slip heterogeneity strongly influences the spatial distribution of the largest tsunami effects along the nearest coastlines. Unfortunately, after an earthquake occurs, the so-called finite-fault models (FFM) describing the coseismic on-fault slip pattern becomes available over time scales that are incompatible with early tsunami warning purposes, especially in the near field. 
Our work aims to characterize the slip heterogeneity in a fast, but still suitable way.
Using finite-fault models to build a starting dataset of seismic events, the characteristics of the fault planes are studied with respect to the magnitude. The patterns of the slip distribution on the rupture plane, analysed with a cluster identification algorithm, reveal a preferential single-asperity representation that can be approximated by a two-dimensional Gaussian slip distribution (2D GD). The goodness of the 2D GD model is compared to other distributions used in literature and its ability to represent the slip heterogeneity in the form of the main asperity is proven. The magnitude dependence of the 2D GD parameters is investigated and turns out to be of primary importance from an early warning perspective.
The Gaussian model is applied to the 16 September 2015 Illapel, Chile, earthquake and used to compute early tsunami predictions that are satisfactorily compared with the available observations. The fast computation of the 2D GD and its suitability in representing the slip complexity of the seismic source make it a useful tool for the tsunami early warning assessments, especially for what concerns the near field.
     
  
  
    
    
      Tipologia del documento
      Tesi di dottorato
      
      
      
      
        
      
        
          Autore
          Baglione, Enrico
          
        
      
        
          Supervisore
          
          
        
      
        
      
        
          Dottorato di ricerca
          
          
        
      
        
      
        
          Ciclo
          32
          
        
      
        
          Coordinatore
          
          
        
      
        
          Settore disciplinare
          
          
        
      
        
          Settore concorsuale
          
          
        
      
        
          Parole chiave
          Finite-Fault Models; On-fault slip distribution; Fitting of Single-Asperity Faults; Tsunami Hazard; Tsunami Early Warning
          
        
      
        
          URN:NBN
          
          
        
      
        
          DOI
          10.48676/unibo/amsdottorato/9252
          
        
      
        
          Data di discussione
          27 Marzo 2020
          
        
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Tesi di dottorato
      
      
      
      
        
      
        
          Autore
          Baglione, Enrico
          
        
      
        
          Supervisore
          
          
        
      
        
      
        
          Dottorato di ricerca
          
          
        
      
        
      
        
          Ciclo
          32
          
        
      
        
          Coordinatore
          
          
        
      
        
          Settore disciplinare
          
          
        
      
        
          Settore concorsuale
          
          
        
      
        
          Parole chiave
          Finite-Fault Models; On-fault slip distribution; Fitting of Single-Asperity Faults; Tsunami Hazard; Tsunami Early Warning
          
        
      
        
          URN:NBN
          
          
        
      
        
          DOI
          10.48676/unibo/amsdottorato/9252
          
        
      
        
          Data di discussione
          27 Marzo 2020
          
        
      
      URI
      
      
     
   
  
  
  
  
  
    
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