Signal And Noise Separation: Art And Science

The separation of signal and noise is a central issue in seismic data processing. The noise is both random<br>and coherent in nature, the coherent part often masquerading as signal. We present some approaches to signal<br>isolation, in which stacking is a central concept. Our methodology is to transform the data to a domain where<br>noise and signal are separable, a goal that we attain by means of inversion. We will illustrate our ideas with some<br>of our favourite transformations, wavelets, eigenvectors, and Radon transforms. We end with the notion of risk,<br>baseball and the Stein estimator.