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Computation of the gradient vector

The gradient vector defined in equation (5) is the sum of traveltime residual term ${\bf g}^{(1)}$ and condition term ${\bf g}^{(2)}$. The computation of the condition term is straightforward because matrix ${\bf B}$ is given. In principle, we can compute the traveltime residual term by first computing matrix ${\bf A}$, and then performing the matrix multiplication. However, matrix ${\bf A}$ has $N\times M$ elements that are functions of r; hence, both its calculation and storage are of high cost. If we can compute the vector ${\bf g}^{(1)}$ directly using a finite difference method, we can considerably reduce the cost in computation time and storage.

If we solve the first-order linear PDE (9) using the method of characteristics, we find that
\begin{displaymath}
a_{ij}(r)=\int^{s_i(r)}_0 \beta_j(\hat{x}_i(\xi,r),\hat{z}_i(\xi,r)) d\xi,\end{displaymath} (10)
where $\hat{x}_i(s,r)$ and $\hat{z}_i(s,r)$ are the coordinates of the ray that starts from the number i source and ends at the receiver at depth r, and s is the arc-length of the ray. Clearly, the elements of matrix ${\bf A}$ are the integrations of the basis functions along rays. The component of the gradient vector can be computed as follows:  
 \begin{displaymath}
\begin{array}
{lll}
g^{(1)}_j & = & \displaystyle{\int_{r}\s...
 ...int_z \beta_j(x,z)
\delta t_i(\hat{r}_i(x,z))}Jdxdz,\end{array}\end{displaymath} (11)
where J is the Jacobian of the coordinate transformation:
\begin{displaymath}
J=
\left\vert
\begin{array}
{ll}
\displaystyle{\partial s \o...
 ...al x}\right)^2+
 \left({\partial r \over \partial z}\right)^2}.\end{displaymath} (12)
The last step of equation (11) is an area integration that is easy to implement. Appendix A shows that function $r=\hat{r}_i(x,z)$ satisfies the first-order linear PDE, as follows:  
 \begin{displaymath}
{\partial \tau_i \over \partial x}{\partial \hat{r}_i \over ...
 ...tau_i \over \partial z}{\partial \hat{r}_i \over \partial z}=0,\end{displaymath} (13)
with the initial condition that function $\hat{r}_i(x,z)$ is equal to the receiver depth at each receiver location. We can use a finite difference algorithm, similar to the one used for traveltime calculation, to solve equation (13). The algorithm should start from receiver locations, and extrapolate function $\hat{r}_i(x,z)$ in the opposite directions of the wave propagations, towards the source.


previous up next print clean
Next: Computation of the conjugate Up: FINITE DIFFERENCE METHODS Previous: Computation of matrix
Stanford Exploration Project
12/18/1997