Summary
Detection of diffractions is an essential step on diffraction imaging techniques. Due to their smaller amplitudes regard- ing reflection events, diffraction events are usually treated as noise in standard seismic processing. Diffraction imaging is often used to identify subsurface scattering features with enhanced resolution in comparison to conventional seismic reflection imaging. Several techniques have been presented in literature for separation of diffracted from reflected events. One way is to analyze amplitudes along diffraction time curves in common-offset sections, where it is easier to perceive differences between diffraction and reflection events. Known pat- tern recognition methods can be used to separate the events. We analyze automatic detection of diffraction points using a two-class k Nearest-Neighbours (kNN) and we present a routine for detection of diffractions using Support Vector Machines (SVM).We evaluate the ability of each method to detect scattering features, using synthetic seismic models. Results indicate that kNN method is more robust to noise and velocity model variation. On the other hand, SVM sensitiveness to velocity model can be useful on velocity analysis of scattering events.
