Towards robust structure-based enhancement and horizon picking in 3-D seismic data
We present a novel structure-enhancing adaptive filter guided by features derived from the gradient structure tensor. We employ this filter to reduce noise in seismic data and to assist in generating seed points for initializing an automatic horizon picking algorithm. In addition, our algorithm takes seismic attributes into consideration to reduce the possibilities of false horizon generation and fault crossing. Comparative experimental results are presented to highlight the potential of our approach.