Blind Separation of Ground-Roll Using Interband Morphological Similarity and Pattern Coding

Ground-roll is a common coherent noise in land seismic records. It has a low frequency, low velocity, and, yet, strong energy, which often conceals important information about reflections. Various approaches were proposed to suppress or remove ground-roll from reflections. The main difficulty of this task is the accurate separation of the ground-roll and reflections without damaging the morphological structure and frequency characteristics of both waves. In this article, we directly aim at the separation in the low-frequency band, where ground-roll and reflections are overlapped, and the recovery of low-frequency reflections deteriorated by ground-roll noise. First, we explore the morphological self-similarity of different frequency bands in seismic data and then utilize this similarity to synthesize pseudolow-band reflections from higher frequency band reflections that contain no ground-roll contamination. After the pseudolow-band reflections are generated, the pattern coding method is then applied to learn patterns from pseudolow-band reflections. The learned pattern dictionary is used to reconstruct the low-band reflections from the mixture of ground-roll and low-band reflections. After the whole scheme, recovered low-band reflections and clean reflections in other frequency bands are combined to form the recovered reflections. Detailed analysis is conducted to illustrate the validity of the scheme, and experiments on both synthetic data and real-land records show promising results.