DUET Algorithm Exploiting Sparsity for Effective Seismic Source Separation

Abstract

Seismic Signal processing involves the separation of seismic sources from a mixture of signal sources. Seismic source separation is gaining importance today, as we require separation techniques for modeling a seismic activity and thus predicting the occurrence of seismic activity. Despite the vast amount of research in this field, the signal processing and event parameters discrimination algorithms have not yet fully come of age. This paper gives a description of different methods for seismic source separation and comparison of commonly adopted algorithms for these methods. This paper also overview the need of sparsity which makes signal separation effective. The problem with seismic sources is that its origin is unknown due to the fact that multiple sources are simultaneously active. The task is to separate out these sources and reconstruct the signals back. Here the idea of sparsity is employed in order for efficient reconstruction of signals. An important property of sparse signal processing, that allows efficient signal reconstruction, is that the information rate of a continuous time signal may be much smaller than that suggested by its bandwidth.