Chao Li

In marine seismic exploration, SRME (surface-related multiple elimination) predicts multiples and then subtracts them from the recorded data with a matching filter; the classical route uses either a fixed-length stationary filter or a non-stationary regression with a local smoothness constraint, which limits it where signal energy changes rapidly. PDL replaces that matching filter with a self-supervised neural network trained under the adaptive matching filtering equation, cutting the seismic profile into many patches so training yields a globally optimal non-stationary match. On synthetic data and the Viking Graben field dataset, PDL leaves fewer multiple residuals than the classical non-stationary least-squares matching filter and better preserves weak primaries and waveform continuity.

In marine seismic exploration, SRME (surface-related multiple elimination) predicts multiples and then subtracts them from the recorded data with a matching filter; the classical route uses either a fixed-length stationary filter or a non-stationary regression with a local smoothness constraint, which limits it where signal energy changes rapidly. PDL replaces that matching filter with a self-supervised neural network trained under the adaptive matching filtering equation, cutting the seismic profile into many patches so training yields a globally optimal non-stationary match. On synthetic data and the Viking Graben field dataset, PDL leaves fewer multiple residuals than the classical non-stationary least-squares matching filter and better preserves weak primaries and waveform continuity.