Anisotropic wave-equation migration velocity analysis (WEMVA) requires fast and accurate wave
modeling at all angles. I use an optimized implicit finite difference one-way propagation engine
to improve both the efficiency and accuracy of this process. In this implicit finite difference scheme, anisotropic
parameters

and

are mapped into two finite difference coefficients,

and

.
When computing the perturbed wavefields from model perturbation, I apply a chain rule to link the
wave equation with the actual anisotropic parameters via the finite difference coefficients. I test
the implementation by impulse responses in both 2D and 3D. The sensitivity kernels for wave-equation
reflection tomography confirm the theoretical understanding that waves have a higher sensitivity
for

at large angles and a higher sensitivity for vertical velocity at small angles.