Hybrid-norm and Fortran 2003: Separating the physics from the solver |

In the simplest case where we are using steepest descent to solve the linear least squares inversion, we estimate by mapping the initial residual (in this simple case ) back into the same space as the model to form a gradient vector by applying the adjoint of . We then map the gradient vector back into data-space by applying to form . Finally, we find the scaling factor of that will make as small as possible. We then repeat this procedure until is suitably small. More complex inversion approaches are normally built on this basic concept.

Hybrid-norm and Fortran 2003: Separating the physics from the solver |

2010-11-26