Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser
Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser
Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
4.6. SUMMARY 74<br />
4.6 Summary<br />
In this chapter, I surveyed modeling methods in the f − x domain. Canales (1984)<br />
method was reviewed and extensions <strong>of</strong> this method to nonlinear problems were<br />
explored.<br />
Events with nonlinear moveouts can be modeled using nonlinear terms <strong>of</strong> a<br />
<strong>Volterra</strong> series. Events with complex waveforms need additional prediction coeffi-<br />
cients in order to properly model the data.<br />
It is clear that linear prediction fails to model data sets with curvature; nonlinear<br />
predictions can accurately model these data. I cannot claim, however, that the<br />
linear part <strong>of</strong> a <strong>Volterra</strong> series models the linear events and that the nonlinear<br />
kernels are modeling the hyperbolic events.