In this paper, a new parallel modeling and inversion algorithm using a Stochastic Global Integral and Local Differential equation (SGILD) is presented. The authors 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, and are important for the prediction of oil, gas, coal, and geothermal energy reservoirs in geophysical exploration.
ark: ark:/67531/metadc710929
S Codes
United States. Department of Energy. Office of Energy Research.
Li, J.
Stochastic Processes
Electromagnetic Surveys
Algorithms
New 3D parallel SGILD modeling and inversion
Seismic Surveys
rep-no: LBNL--42252
osti: 676981
58 Geosciences
17 p.
Calculation Methods
other: DE98059389
Lawrence Berkeley National Laboratory. Earth Sciences Division.
Majer, E.
18. annual international conference on predictability quantifying uncertainty in models of complex phenomena, Los Alamos, NM (United States), 11-15 May 1998
Hydrology
grantno: AC03-76SF00098
Xie, G.
1998-09-01
Geologic Models
rep-no: CONF-9805106--