In reservoir monitoring, changes in seismic attributes are desirably associated with changes in reservoir properties. However, seismic data are also very sensitive to acquisition parameters, recording system and near-surface conditions, which can also change with time (“4D noise”). Match filters are routinely applied in 4D seismic processing to compensate for the phase and amplitude differences between baseline and monitor data to suppress the 4D noise and highlight actual changes in the subsurface. The conventional Least-Squares matching filter derived by minimizing the energy in the square of the difference between baseline and matched-monitor datasets has a number of shortcomings when applied to low signal-to-noise data e.g. when the filter is applied pre-stack and/or on low-fold data. These shortcomings include lack of symmetry with respect to interchange of base and monitor volumes, and the tendency to mute the energy of the matched seismic volume. Alternative methods referred to as a spectrum balancing match filters are developed that produce a matched monitor trace with the same power spectrum as the baseline. This concept is modified to tackle more complicated scenarios such as matching streamer and OBN data and developing joint matching and whitening filters that are applied to both datasets.


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