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Adjoint method from the perturbation theory

A perturbation, $ \delta c$ , of the velocity model $ c$ , induces a perturbation $ \delta {\bf p}$ in the source wavefield vector $ {\bf p}$ , a perturbation $ \delta {\bf q}$ in the receiver wavefield vector $ {\bf q}$ , a perturbation $ \delta {\bf I}$ in the extended image cube $ {\bf I}$ , and hence a perturbation $ \delta J$ in the objective function $ J$ . To the first order and using chain rule, $ \delta J$ and $ \delta c$ have following relationship:

$\displaystyle \delta J = \sum_{\bf h} \frac{\partial J}{\partial I_{\bf h}} \fr...
...ial I_{\bf h}}{\partial {\bf q}} \frac{\partial {\bf q}}{\partial c} \delta c .$ (16)

Now we can define the gradient by the back-projection of a unit perturbation in the objective function:
$\displaystyle \nabla_c J$ $\displaystyle =$ $\displaystyle \sum_{\bf h}\left ( \frac{\partial J}{\partial I_{\bf h}} \frac{\...
...ial I_{\bf h}}{\partial {\bf q}} \frac{\partial {\bf q}}{\partial c} \right )^*$  
  $\displaystyle =$ $\displaystyle \left(\nabla_c J\right)_1 + \left(\nabla_c J\right)_2.$ (17)

Let's analyze the first term in equation 17 in detail, and the second term follows the same reasoning.
$\displaystyle \left(\nabla_c J\right)_1$ $\displaystyle =$ $\displaystyle \sum_{\bf h} \left ( \frac{\partial J}{\partial I_{\bf h}} \frac{...
...ial I_{\bf h}}{\partial {\bf p}} \frac{\partial {\bf p}}{\partial c} \right )^*$  
  $\displaystyle =$ $\displaystyle \sum_{\bf h} \left ( \frac{\partial {\bf p}}{\partial c} \right )...
...al {\bf p}} \right )^* \left ( \frac{\partial J}{\partial I_{\bf h}} \right )^*$  
  $\displaystyle =$ $\displaystyle \sum_{\bf h} {\bf p}^* \left(-\frac{\partial {\bf L}}{\partial c}...
...\partial {\bf p}}\right)^* \left(\frac{\partial J}{\partial I_{\bf h}}\right)^*$  
  $\displaystyle =$ $\displaystyle {\bf p}^* \left(-\frac{\partial {\bf L}}{\partial c}\right)^* {\b...
...\partial {\bf p}}\right)^* \left(\frac{\partial J}{\partial I_{\bf h}}\right)^*$ (18)

where
$\displaystyle \left ( \frac{\partial J}{\partial I_{\bf h}} \right )^*$ $\displaystyle =$ $\displaystyle {\bf h}^*{\bf h} I_{\bf h},$ (19)

and
$\displaystyle \left ( \frac{\partial I_{\bf h}}{\partial {\bf p}} \right )^*$ $\displaystyle =$ $\displaystyle ({\bf S}_{+{\bf h}})^* {\bf M}^*({\bf S}_{-{\bf h}} {\bf q})$  
  $\displaystyle =$ $\displaystyle {\bf S}_{-{\bf h}} {\bf M}^*({\bf S}_{-{\bf h}} {\bf q}).$ (20)

Plugging equation 19 and 20 into equation 18, we can rewrite equation 18 explicitly as follows:

$\displaystyle \left(\frac{\partial J}{\partial c}\right)_1 = {\bf p}^* \left (-...
... h} {\bf S}_{-{\bf h}} {\bf M}^*({\bf S}_{-{\bf h}} {\bf q}) {\bf h}^2I_{\bf h}$ (21)

Similarly, we can obtain the explicit form for the second term in equation 17:

$\displaystyle \left(\frac{\partial J}{\partial c}\right)_2 = {\bf q}^* \left (-...
...f h} {\bf S}_{+{\bf h}} {\bf M} ({\bf S}_{+{\bf h}} {\bf p}) {\bf h}^2I_{\bf h}$ (22)

Substituting equation 21 and equation 22 for the corresponding terms in equation 17, we now have derived the explicit form for the DSO gradient.


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Next: Lagrangian augmented functional method Up: Migration Velocity Analysis Gradients Previous: Migration Velocity Analysis Gradients

2012-05-10