Abstract
Due to incomplete and non-uniform coverage of the acquisition system and dead traces, real seismic data always has some missing traces which affects the performance of a multi-channel algorithm, such as Surface-Related Multiple Elimination (SRME), imaging and inversion. Therefore, it is necessary to interpolate seismic data. Dreamlet transform has been successfully used in the modeling of seismic wave propagation and imaging, and this paper explains the application of dreamlet transform to seismic data interpolation. With L0 norm constraint and Projection Onto Convex Sets (POCS) method, performances of dreamlet-based and curvelet-based interpolation are compared in terms of recovered signal to noise ratio (SNR) and convergence rate. Tests on synthetic and real seismic data demonstrate that dreamlet transform-based method has superior performance to curvelet transform-based method.
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