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Regularized joint-inversion of multiple images: RJMI

The data modeling process for three seismic datasets (a baseline and two monitors) over an evolving earth model can be written as

$\displaystyle \left [ \begin{array}{ccc} {\bf L}_{0} & {\bf0} & {\bf0}  {\bf0...
...gin{array}{ccc} {\bf d}_{0}  {\bf d}_{1}  {\bf d}_{2} \end{array} \right ],$ (A-7)

where $ {\bf d}_{0}$, $ {\bf d}_{1}$ and $ {\bf d}_{2}$ are respectively datasets for the baseline, first and second monitor, $ {\bf m}_{0}$, $ {\bf m}_{1}$, and $ {\bf m}_{2}$ are the baseline and monitor reflectivity models. The linear operators ( $ {\bf L}_{0}$, $ {\bf L}_{1}$ and $ {\bf L}_{2}$) define the modeling/acquisition experiments for datasets $ {\bf d}_{0}$, $ {\bf d}_{1}$ and $ {\bf d}_{2}$ respectively. The least-squares solution to equation A-7 is given as

$\displaystyle \left [ \begin{array}{ccc} {\bf {L'}_0 L_0}&{\bf0 }&{\bf0} {\bf...
...begin{array}{c} {\bf d}_{0}  {\bf d}_{1}  {\bf d}_{2} \end{array} \right ],$ (A-8)

where the symbol $ '$ denotes transposed complex conjugate.

We rewrite equation A-8 as

$\displaystyle \left [ \begin{array}{ccc} {\bf H_0}&{\bf0}&{\bf0} {\bf0}&{\bf ...
...c} \tilde {\bf m_0} \tilde {\bf m_1} \tilde {\bf m_2} \end{array} \right ],$ (A-9)

where $ \tilde {\bf m_i}$ is the migrated image from the $ {{\bf i}^{th}}$ survey, and $ {\bf H_i}$ is the corresponding Hessian matrix. Introducing spatial and temporal constraints into equation A-9 we obtain

$\displaystyle \left ( \left [ \begin{array}{ccc} {\bf H_0}&{\bf0}&{\bf0} {\bf...
... \tilde {\bf m_0}  \tilde {\bf m_1}  \tilde {\bf m_2} \end{array} \right ].$ (A-10)

Equation A-10 can be generalized to an arbitrary number of surveys as follows

$\displaystyle \left [ {\bf\Xi } + {\bf\Re } + {\bf\Gamma} \right ] \left [ \hat{{\bf M}} \right ] = \left [ \tilde {\bf M} \right ],$ (A-11)

where, $ {\bf\Xi }$ is the Hessian operator, defined as

$\displaystyle {\bf\Xi }= \left [ \begin{array}{cccccc} {\bf H_0}&{\bf0}&{\bf0}&...
...&{\bf0} {\bf0}&{\bf0}&{\bf0}&{\bf ...}&{\bf0}&{\bf H_N} \end{array} \right ].$ (A-12)

The spatial and temporal regularization operators, $ {\bf\Re} $ and $ {\bf\Gamma}$ are defined as

\begin{displaymath}\begin{array}{c} {\bf\Re = R' R}, {\bf\Gamma = \Lambda' \Lambda}, \end{array}\end{displaymath} (A-13)

where,

$\displaystyle {\bf R }= \left [ \begin{array}{cccccc} {\bf R_{0}}&{\bf0}&{\bf0}...
...}&{\bf0} {\bf0}&{\bf0}&{\bf0}&{\bf0}&{\bf0}&{\bf R_N} \end{array} \right ],$ (A-14)

and,

$\displaystyle {\bf\Lambda }= \left [ \begin{array}{cccccc} {\bf\Lambda_{0}}&{\b...
...\bf0 }&{\bf0 }&{\bf ...}&{\bf0 }&{\bf0 }&{\bf\Lambda_{N}} \end{array} \right ].$ (A-15)

The input vector into the RJMI formulation, $ {\tilde{\bf M}}$ is given as

$\displaystyle \tilde{{\bf M}} = \left [ \begin{array}{cc} \tilde{\bf m}_{0}  ...
... {\bf :}  \tilde{\bf m}_{N-1}  \tilde{\bf m}_{N}  \end{array} \right ],$ (A-16)

while the inversion targets are in the vector:

$\displaystyle \hat{{\bf M}} = \left [ \begin{array}{cc} \hat{\bf m}_{0}  \hat...
...{2}  {\bf :}  \hat{\bf m}_{N-1}  \hat{\bf m}_{N}  \end{array} \right ].$ (A-17)


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Next: Regularized joint-inversion for image Up: Joint-Inversion formulations for multiple Previous: Regularized joint-inversion

2009-04-13