next up previous print clean
Next: Data and Model preconditioning Up: Practical implementation of ICO Previous: Data-space preconditioning

Model-space preconditioning

This approach is based on a post-multiplication of the matrix $\bold L$ by a diagonal matrix ${\bf C^{-1}}$ where the sums of the elements from each column of $\bold L$ are along the diagonal of ${\bf C}$.The preconditioning operator introduces a new model $\bold x$ given by
\begin{displaymath}
\bold x = \bold C \bold m
\EQNLABEL{mod-prec}\end{displaymath} (56)
By the preconditioning transformation, we have recast the original inversion relation equ1 into
\begin{displaymath}
\bold d = \bold L \bold C^{-1} \bold x.
\EQNLABEL{prec}\end{displaymath} (57)

After solving for $\bold x$ we easily compute $\bold m = \bold C^{-1} \bold x$.

Given that each column of $\bold L$ corresponds to an output bin, $\bold C^{-1}$is normalization by the coverage after AMO.


next up previous print clean
Next: Data and Model preconditioning Up: Practical implementation of ICO Previous: Data-space preconditioning
Stanford Exploration Project
1/18/2001