Surface-Consistent Matching Filters for Time-Lapse Processing

We design a suite of surface-consistent matching filters for processing time-lapse seismic data in a surface-consistent manner. By matching filter we mean a convolutional filter that minimizes the sum-squared difference between two signals. Such filters are sometimes called shaping filters. The frequency-domain surface-consistent design equations are similar to those for surface-consistent deconvolution except that the data term is the spectral ratio of two surveys. We compute the spectral ratio in the time domain by first designing trace-sequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factors the trace-sequential matching filters into four surface-consistent operators: source, receiver, offset, and midpoint. We present a synthetic time-lapse example with nonrepeatable acquisition parameters and near-surface and subsurface model variability. Our matching filter algorithm significantly reduces the nonrepeatability often observed in time-lapse data sets.