Full-waveform inversion of elastic data with a corrected acoustic wave propagator

2017 ◽  
Author(s):  
Yi Li* ◽  
Chong Wang ◽  
Hanming Gu
2018 ◽  
Vol 37 (1) ◽  
pp. 69-72 ◽  
Author(s):  
Mathias Louboutin ◽  
Philipp Witte ◽  
Michael Lange ◽  
Navjot Kukreja ◽  
Fabio Luporini ◽  
...  

This is the second part of a three-part tutorial series on full-waveform inversion (FWI) in which we provide a step-by-step walk through of setting up forward and adjoint wave equation solvers and an optimization framework for inversion. In Part 1 ( Louboutin et al., 2017 ), we showed how to use Devito ( http://www.opesci.org/devito-public ) to set up and solve acoustic wave equations with (impulsive) seismic sources and sample wavefields at the receiver locations to forward model shot records. Here in Part 2, we will discuss how to set up and solve adjoint wave equations with Devito and, from that, how we can calculate gradients and function values of the FWI objective function.


2020 ◽  
Vol 172 ◽  
pp. 103876
Author(s):  
Jinwei Fang ◽  
Hui Zhou ◽  
Qingchen Zhang ◽  
Hanming Chen ◽  
Pengyuan Sun ◽  
...  

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. R257-R271 ◽  
Author(s):  
Òscar Calderón Agudo ◽  
Nuno Vieira da Silva ◽  
Michael Warner ◽  
Joanna Morgan

Full-waveform inversion (FWI) is a technique used to obtain high-quality velocity models of the subsurface. Despite the elastic nature of the earth, the anisotropic acoustic wave equation is typically used to model wave propagation in FWI. In part, this simplification is essential for being efficient when inverting large 3D data sets, but it has the adverse effect of reducing the accuracy and resolution of the recovered P-wave velocity models, as well as a loss in potential to constrain other physical properties, such as the S-wave velocity given that amplitude information in the observed data set is not fully used. Here, we first apply conventional acoustic FWI to acoustic and elastic data generated using the same velocity model to investigate the effect of neglecting the elastic component in field data and we find that it leads to a loss in resolution and accuracy in the recovered velocity model. Then, we develop a method to mitigate elastic effects in acoustic FWI using matching filters that transform elastic data into acoustic data and find that it is applicable to marine and land data sets. Tests show that our approach is successful: The imprint of elastic effects on the recovered P-wave models is mitigated, leading to better-resolved models than those obtained after conventional acoustic FWI. Our method requires a guess of [Formula: see text] and is marginally more computationally demanding than acoustic FWI, but much less so than elastic FWI.


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