Adaptive Curvelet Domain Primary-Multiple Separation

We describe the implementation of using curvelet domain for separating multiples from primary events in seismic data and subsequently removing the multiples from noisy seismic data. Introduced by the sparsity of curvelet coefficients of seismic data, an optimization problem was formularized by incorporating L1- and L2-norms, which is then iteratively solved. We show that our approach gives superior performance than the conventional least-square separation method in attenuating multiples and incoherent noise, and with better preservation of primary events. Moreover, our particular strategy in globally adapting the model widens the scope of application of this method for multiple models predicted from various methods. We demonstrate the application of our approach on synthetic and field data examples.