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# Higher accuracy linearizations

As noted earlier, the approximation
 (9)
corresponds to an explicit numerical solution to the differential equation (2). However, this is neither the only possible solution, nor the most accurate, and furthermore it is only conditionally stable.

We can, however, solve Equation (2) using other numerical schemes. Two possibilities are implicit numerical solutions, where we approximate
 (10)
or bilinear numerical solutions, where we approximate
 (11)
Equations (9) and (10) are first order, but Equation (11) is second order accurate as a function of the phase .Numerical schemes based on Equation (9) are conditionally stable, but numerical schemes based on Equations (10) and (11) are unconditionally stable.

In the context of partial differential equations, the bilinear approximation (11) is known under the name of Crank-Nicolson and has been extensively used in migration by downward-continuation using the paraxial wave equation Claerbout (1985). Figures 1 and 2 compare the approximations in Equations (9), (10) and (11) as a function of phase. Both the explicit and implicit solutions lead to errors in amplitude and phase, while the bilinear solution leads just to errors in phase (Figure 2).

 unit Figure 1 Explicit, bilinear and implicit approximations plotted on the unit circle. The solid line corresponds to the exact exponential solution.

 exap Figure 2 Amplitude and phase errors for the explicit, bilinear and implicit approximations.

If, for notation simplicity, we define
 (12)
the WEMVA equation (7) can be written as
 (13)
and so the linearizations corresponding to the explicit, bilinear and implicit solutions respectively become
 (14)
Aparently, just the first equation in (14) provides a linear relationship between and . However, a simple re-arrangement of terms leads to
 (15)

For MVA, both the background () and perturbation wavefields () are known, so it is not a problem to incorporate them in the linear operator. In any of the cases described in Equation (15), the approximations can be symbolically written using the fitting goal
 (16)
where the data is the wavefield perturbation, and the model is the slowness perturbation. The same operator is used for inversion in all situations, the only change being in the wavefield that is fed into the linear operator. Therefore, the new operators are not more expensive than the Born operator.

All linear relationships in Equation (15) belong to a family of approximations of the general form
 (17)
The various approximations can be obtained using appropriate values for the parameter .All forms of Equation (17), however, are approximations to the exact non-linear relation (13), therefore they are all likely to break for large values of the phase, or equivalently large values of the slowness perturbation or frequency. Nevertheless, these approximations enable us to achieve higher accuray in slowness estimation as compared to the simple Born approximation.

An interesting comparison can be made between the extreme members of the sequence given by Equation (17): for we use the background wavefield , and for we use the full wavefield . The physics of scattering would recommend that we use the later form, since the scattered wavefield () is generated by the total wavefield (), and not by an approximation of it (), thus naturally accounting for multiple scattering effects. The later situation also corresponds to what is known in the scattering literature as wavefield renormalization Wang (1997). The details of these ideas and their implications remain open for future research.

Finally, we note that Equation (17) cannot be used for forward modeling of the wavefield perturbations , except for the particular case , since the output quantity is contained in the operator itself. However, we can use this equation for inversion for any choice of the parameter .

Next: Newton's method and WEMVA Up: Sava and Fomel: WEMVA Previous: Born wave-equation MVA
Stanford Exploration Project
6/7/2002