Statistics – Computation
Scientific paper
Sep 2005
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005geoji.162..685s&link_type=abstract
Geophysical Journal International, Volume 162, Issue 3, pp. 685-695.
Statistics
Computation
6
Scientific paper
We introduce the concept of multi-objective optimization to cast the regularized inverse direct current resistivity problem into a general formulation. This formulation is suitable for the efficient application of a genetic algorithm, which is known as a global and non-linear optimization tool. The genetic inverse algorithm generates a set of solutions reflecting the trade-off between data misfit and some measure of model features. Examination of such an ensemble is highly preferable to classical approaches where just one `optimal' solution is examined since a better overview over the range of possible inverse models is gained. However, the computational cost to obtain this ensemble is enormous. We demonstrate that at the current state of computer performance inversion of 2-D direct current resistivity data using genetic algorithms is possible if state-of-the-art computational techniques such as parallelization and efficient 2-D forward operators are applied.
Börner Ralph-Uwe
Schwarzbach Christoph
Spitzer Klaus
No associations
LandOfFree
Two-dimensional inversion of direct current resistivity data using a parallel, multi-objective genetic algorithm does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Two-dimensional inversion of direct current resistivity data using a parallel, multi-objective genetic algorithm, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Two-dimensional inversion of direct current resistivity data using a parallel, multi-objective genetic algorithm will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1286573