Complete Implementation of the Green's Function Based Time Reverse Imaging and Sensitivity Analysis of Reversed Time Tsunami Source Inversion

2017 ◽  
Vol 44 (19) ◽  
pp. 9844-9855 ◽  
Author(s):  
M. J. Hossen ◽  
P. R. Cummins ◽  
K. Satake
Keyword(s):  
Sensitivity Analysis ◽  
Green’S Function ◽  
Green's Function ◽  
Tsunami Source ◽  
Source Inversion ◽  
Tsunami Source Inversion ◽  
Reversed Time

The Green’s function method of sensitivity analysis in chemical kinetics

1978 ◽  
Vol 69 (11) ◽  
pp. 5180-5191 ◽  
Author(s):  
Jenn‐Tai Hwang ◽  
Eugene P. Dougherty ◽  
Suzanne Rabitz ◽  
Herschel Rabitz
Keyword(s):  
Sensitivity Analysis ◽  
Green’S Function ◽  
Chemical Kinetics ◽  
Green's Function ◽  
Function Method ◽  
Green’S Function Method

scholarly journals Chemical Source Inversion Using Assimilated Constituent Observations in an Idealized Two-Dimensional System

2009 ◽  
Vol 137 (9) ◽  
pp. 3013-3025
Author(s):  
Andrew Tangborn ◽  
Robert Cooper ◽  
Steven Pawson ◽  
Zhibin Sun
Keyword(s):  
Kalman Filter ◽  
Green’S Function ◽  
Green's Function ◽  
Transport Model ◽  
Chemical Constituents ◽  
Gaussian Model ◽  
Satellite Observation ◽  
Dimensional System ◽  
Two Dimensional ◽  
Source Inversion

Abstract A source inversion technique for chemical constituents is presented that uses assimilated constituent observations rather than directly using the observations. The method is tested with a simple model problem, which is a two-dimensional Fourier–Galerkin transport model combined with a Kalman filter for data assimilation. Inversion is carried out using a Green’s function method and observations are simulated from a true state with added Gaussian noise. The forecast state uses the same spectral model but differs by an unbiased Gaussian model error and emissions models with constant errors. The numerical experiments employ both simulated in situ and satellite observation networks. Source inversion was carried out either by directly using synthetically generated observations with added noise or by first assimilating the observations and using the analyses to extract observations. Twenty identical twin experiments were conducted for each set of source and observation configurations, and it was found that in the limiting cases of a very few localized observations or an extremely large observation network there is little advantage to carrying out assimilation first. For intermediate observation densities, the source inversion error standard deviation is decreased by 50% to 90% when the observations are assimilated with the Kalman filter before carrying out the Green’s function inversion.

scholarly journals Source modeling and spectral analysis of the Crete tsunami of 2nd May 2020 along the Hellenic Subduction Zone, offshore Greece

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Mohammad Heidarzadeh ◽  
Aditya Riadi Gusman
Keyword(s):  
Spectral Analysis ◽  
Tide Gauge ◽  
Eastern Mediterranean ◽  
Near Field ◽  
Tsunami Source ◽  
Source Inversion ◽  
Source Modeling ◽  
Crete Island ◽  
Tsunami Source Inversion ◽  
Wave Trains

AbstractTsunami hazard in the Eastern Mediterranean Basin (EMB) has attracted attention following three tsunamis in this basin since 2017 namely the July 2017 and October 2020 Turkey/Greece and the May 2020 offshore Crete Island (Greece) tsunamis. Unique behavior is seen from tsunamis in the EMB due to its comparatively small size and confined nature which causes several wave reflections and oscillations. Here, we studied the May 2020 event using sea level data and by applying spectral analysis, tsunami source inversion, and numerical modeling. The maximum tsunami zero-to-crest amplitudes were measured 15.2 cm and 6.5 cm at two near-field tide gauge stations installed in Ierapetra and Kasos ports (Greece), respectively. The dominant tsunami period band was 3.8–4.7 min. We developed a heterogeneous fault model having a maximum slip of 0.64 m and an average slip of 0.28 m. This model gives a seismic moment of 1.13 × 1019 Nm; equivalent to Mw 6.67. We observed three distinct wave trains on the wave record of the Ierapetra tide gauge: the first and the second wave trains carry waves with periods close to the source period of the tsunami, while the third train is made of a significantly-different period of 5–10 min.

scholarly journals Green's function retrieval with Marchenko equations: A sensitivity analysis

2013 ◽  
Author(s):  
Jan Thorbecke ◽  
Joost van der Neut ◽  
Kees Wapenaar
Keyword(s):  
Sensitivity Analysis ◽  
Green’S Function ◽  
Green's Function
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