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  • 1
    Publication Date: 2018-09-01
    Print ISSN: 1742-6588
    Electronic ISSN: 1742-6596
    Topics: Physics
    Published by Institute of Physics
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  • 2
    Publication Date: 2014-05-22
    Description: Determination of sea surface deformation generated by earthquakes is crucial to the success of tsunami modeling. Using waveforms recorded at measurement stations and assuming that the rupture velocity is much faster than the tsunami wave celerity, sea surface deformation caused by a tsunamigenic earthquake can be inferred through an inversion operation using the Green's function technique. However, this inversion method for tsunami waveforms possesses a limitation, in that the inverse matrix does not always exist because of the non-uniqueness of the solution. In addition to the large number of unknown parameters, which might produce many local optima on the misfit function measure, the search towards optimality is confined by the uniform distance of unit sources used in the regular Green's function. This study proposes a new method to both optimize the determination of the unknown parameters and introduce a global optimization method for tsunami waveform inversion. The method has been tested using an artificial tsunami source with real bathymetry data. A significant improvement is achieved by stochastically searching for an optimal distribution of unit source locations prior to the inversion.
    Electronic ISSN: 2195-9269
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 3
    Publication Date: 2015-01-30
    Description: In tsunami waveform inversion using the conventional Green's function technique, an optimal solution is sometimes difficult to obtain because of various factors. This study proposes a new method to both optimize the determination of the unknown parameters and introduce a global optimization method for tsunami waveform inversion. We utilize a genetic algorithm that further enhanced by a pattern search method to find an optimal distribution of unit source locations prior to the inversion. Unlike the conventional method that characterized by equidistant unit sources, our method generates a random spatial distribution of unit sources inside the inverse region. This leads to a better approximation of the initial profile of a tsunami. The method has been tested using an artificial tsunami source with real bathymetry data. Comparison results demonstrate that the proposed method has considerably outperformed the conventional one in terms of model accuracy.
    Print ISSN: 1561-8633
    Electronic ISSN: 1684-9981
    Topics: Geography , Geosciences
    Published by Copernicus on behalf of European Geosciences Union.
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  • 4
    Publication Date: 2020-02-27
    Description: We propose a method for defining the optimal locations of a network of tsunameters in view of near real-time tsunami forecasting using sea surface data assimilation in the near and middle fields, just outside of the source region. The method requires first the application of the empirical orthogonal function analysis to identify the potential initial locations, followed by an optimization heuristic that minimizes a cost-benefit function to narrow down the number of stations. We apply the method to a synthetic case of the 2015 Mw8.4 Illapel Chile earthquake and show that it is possible to obtain an accurate tsunami forecast for wave heights at near coastal points, not too close to the source, from assimilating data from three tsunameters during 14 min, but with a minimum average time lag of nearly 5 min between simulated and forecasted waveforms. Additional tests show that the time lag is reduced for tsunami sources that are located just outside of the area covered by the tsunameter network. The latter suggests that sea surface data assimilation from a sparse network of stations could be a strong complement for the fastest tsunami early warning systems based on pre-modelled seismic scenarios.
    Print ISSN: 0956-540X
    Electronic ISSN: 1365-246X
    Topics: Geosciences
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  • 5
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-06-27
    Description: The September 2022 Hurricane Ian, which made a landfall as a Category four hurricane, with wind speeds reaching 240 km/h, was among the most destructive hurricanes to hit Florida: at least 77 deaths were reported in Florida and North Carolina, and the total damage was estimated to approximately US$ 63 billion. Along the coast Hurricane Ian generated both a normal (positive) and a reverse (negative) storm surge. A comprehensive data analysis and modelling efforts were undertaken to illuminate processes leading to two types of surges. Mean sea level pressure and wind data from 49 meteorological NOAA and ASOS stations, measured with 1-6 min time step, along with sea level data from 11 NOAA tide gauges, measured with a 1 min time step, were analysed. The ERA5 Reanalysis data were used to assess propagation parameters and synoptic scale properties of Hurricane Ian. Numerical weather prediction High-Resolution Rapid Refresh (HRRR) model, and a parametric wind model of tropical cyclones were both used to estimate temporal evolution of the 10 m wind and mean sea level pressure fields near and over Florida. The models were then used separately to force the Regional Ocean Modelling System (ROMS), and to reproduce positive and negative surges. Differences between two sets of simulations are discussed in detail. It was shown that the main factor governing appearance of positive and negative surge was high spatial changeability of wind field over a relatively small (O(200 km)) area.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 6
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    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Publication Date: 2023-09-12
    Description: Two large earthquakes occurred in the Alaska-Aleutian subduction zone in July 2020 (Mw 7.8) and July 2021 (Mw 8.2), generating tsunamis characterized by considerably longer periods than that typically expected from their moment magnitudes. The 2020 earthquake resulted in approximately 40–90 min tsunami periods (Mulia et al., 2022, GRL; Heidarzadeh and Mulia, 2021, Ocean Eng.). Similarly, the 2021 event exhibited long-period tsunamis of 〉50 min (Mulia et al., 2022, SRL). For comparison, the April 2014 Illapel, Chile, earthquake (Mw 8.2) and the November 2016 Kaikoura, New Zealand, earthquake (Mw 7.8) produced tsunamis with dominant periods ranging from 15 to 21 min (Heidarzadeh et al., 2019, Ocean Eng.). To reveal the underlying cause for such anomalous occurrences, we conducted an inversion analysis using tsunami and geodetic data. Our inversion results indicated the up-dip extent of both earthquakes confined at a depth of ~20 km of the plate interface, which corresponds to the shelf break on the surface. Therefore, the coseismic surface displacement predominantly took place at shallow water depths of ~200 m within the broad continental shelf extending ~120 km offshore. Consequently, it is responsible for the long-period tsunami waves as the water depth is inversely proportional to the period. This geophysical setting is uniquely attributed to the Alaska-Aleutian subduction zone, which is rarely found in other major subduction systems. References: https://doi.org/10.1029/2021GL094937; https://doi.org/10.1785/0220210359; https://doi.org/10.1016/j.oceaneng.2021.109243.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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