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I present a method for missing-trace interpolation of linear events. For a two-dimensional dataset which contains linear events, a post-interpolation spectrum can be estimated from a portion of the original aliased spectrum. The restoration of missing trace data is accomplished by minimizing the energy after applying a filter which has an amplitude spectrum that is inverse to the estimated spectrum.

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