Conditional statistical inverse modeling in groundwater flow by multigrid methods |
| |
Authors: | Volker Schulz Andras Bardossy Rainer Helmig |
| |
Institution: | (1) Interdisciplinary Center for Scientific Computing, University of Heidelberg, Im Neuenheimer Feld 368, D‐69120 Heidelberg, Germany;(2) Institute for Hydraulic Engineering, University of Stuttgart, Pfaffenwaldring 7, D‐70550 Stuttgart, Germany;(3) Institute for Computer Applications in Civil Engineering, Technical University of Braunschweig, Pockelsstrasse 3, D‐38106 Braunschweig, Germany |
| |
Abstract: | Due to the notorious lack of data, stochastic simulation and conditioning of distributed parameter fields is generally acknowledged
as a major task in order to produce realistic prognoses for groundwater flow phenomena, thus honouring the maximum of information
available. In this paper, a new conditioning approach is presented which treats the distributed parameters directly without
projection onto lower dimensional spaces and preserves certain desired statistical properties by explicitly stating them as
constraints for the conditioning optimization problem. Typically, the conditioning task must be performed very often and the
conditioning optimization problems are highly dimensional. Therefore, a second main focus of the paper is on the presentation
of efficient multigrid methods for the solution of the conditioning problems. Numerical results are given for a practical
application problem.
This revised version was published online in July 2006 with corrections to the Cover Date. |
| |
Keywords: | geostatistical inverse modeling multigrid methods large‐ scale optimization nonlinear programming SQP methods |
本文献已被 SpringerLink 等数据库收录! |
|