New 3D parallel SGILD modeling and inversion Page: 6 of 20
This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided to Digital Library by the UNT Libraries Government Documents Department.
The following text was automatically extracted from the image on this page using optical character recognition software:
New parallel SGILD modeling and inversion
Ganquan Xie, Jianhua Li, and Ernest Majer
Earth Sciences Division, Lawrence Berkeley National Laboratory
In this paper, A new parallel modeling and inversion algorithm using a Stochastic
Global Integral and Local Differential equation (SGILD) is presented. We derived
new acoustic integral equations and differential equation for statistical moments
of the parameters and field. The new statistical moments integral equation on the
boundary and local differential equations in domain will be used together to obtain
mean wave field and its moments in the modeling. The new moments global Jacobian
volume integral equation and the local Jacobian differential equations in domain
will be used together to update the mean parameters and their moments in the
inversion. A new parallel multiple hierarchy substructure direct algorithm or direct-
iteration hybrid algorithm will be used to solve the sparse matrices and one smaller
full matrix from domain to the boundary, in parallel. The SGILD modeling and
imaging algorithm has many advantages over the conventional imaging approaches.
The SGILD algorithm can be used for the stochastic acoustic, electromagnetic, and
flow modeling and inversion.
Key words: SGILD; modeling and imaging; stochastic; moments integral and
Seismic, electromagnetic, and hydrology modeling and inversion are important
for the prediction of oil, gas, coal, and geothermal energy reservoirs in geo-
physical exploration. Many imaging works in the geophysical research areas
are used the determinstic frame. The deterministic inversion approaches are
used to obtain the ensemble mean of the random target parameters. Because
the data is incomplete and contaminated by noise, it is reasonable to study
inverse and forward problem in the probability frame and to use stochastic
approaches . There are two ways to study the stochastic inversion, one is
Markov chain Monte Carlo (MCMC) approach, other way is to recover the
statistics moments of the parameters and fields using posterior probability
LBNL Report: LBNL 42252, September 1, 1998 1
Here’s what’s next.
This article can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Article.
Xie, G.; Li, J. & Majer, E. New 3D parallel SGILD modeling and inversion, article, September 1, 1998; Berkeley, California. (https://digital.library.unt.edu/ark:/67531/metadc710929/m1/6/: accessed May 27, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.