@Article{CiCP-28-228,
author = {Geng , YuInnanen , Kristopher A. and Pan , Wenyong},
title = {Subspace Methods in Multi-Parameter Seismic Full Waveform Inversion},
journal = {Communications in Computational Physics},
year = {2020},
volume = {28},
number = {1},
pages = {228--248},
abstract = {<p style="text-align: justify;">In full waveform inversion (FWI) high-resolution subsurface model parameters are sought. FWI is normally treated as a nonlinear least-squares inverse problem,
in which the minimum of the corresponding misfit function is found by updating the
model parameters. When multiple elastic or acoustic properties are solved for, simple
gradient methods tend to confuse parameter classes. This is referred to as parameter
cross-talk; it leads to incorrect model solutions, poor convergence and strong dependence on the scaling of the different parameter types. Determining step lengths in a
subspace domain, rather than directly in terms of gradients of different parameters, is
a potentially valuable approach to address this problem. The particular subspace used
can be defined over a span of different sets of data or different parameter classes, provided it involves a small number of vectors compared to those contained in the whole
model space. In a subspace method, the basis vectors are defined first, and a local minimum is found in the space spanned by these. We examine the application of the subspace method within acoustic FWI in determining simultaneously updates for velocity
and density. We first discuss the choice of basis vectors to construct the spanned space,
from linear updates by distinguishing only the contributions of different parameter
classes towards nonlinear updates by adding the contributions of higher-order perturbations of each parameter class. The numerical character of FWI solutions generated
via subspace methods involving different basis vectors is then analyzed and compared
with traditional FWI methods. The subspace methods can provide better reconstructions of the model, especially for the velocity, as well as improved convergence rates,
while the computational costs are still comparable with the traditional FWI methods.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2018-0087},
url = {http://global-sci.org/intro/article_detail/cicp/16835.html}
}