(1) |

With the notation that is the matrix representing convolution with time series , we can rewrite this desired minimization as a fitting goal [e.g. Claerbout (1998a)],

(2) |

(3) |

For the multiple suppression problem, the vector represents the multiple infested raw data, and the matrix represents convolution with the multiple model. Criterion (1) implies a choice of filter that minimizes the energy in the dataset after multiple removal.

One advantage with working with time-domain filters as opposed to
frequency-domain filters is that the theory can be adapted relatively
easily to address non-stationarity.
Following Claerbout (1998a) and Margrave (1998), we extend the
concept of a filter to that of a non-stationary filter-bank, which in
principle contains one filter for every point in the input/output
space.
For a non-stationary filter-bank, , we identify with the filter corresponding to the location in the
input/output vector, and the coefficient, *f*_{i,j}, with the
coefficient of the filter, .The response of non-stationary filtering with to an impulse
in the location in the input is then .

With a non-stationary convolution filter, , the shaping filter regression normal equations, are massively underdetermined since there is a potentially unique impulse response associated with every point in the dataspace. We need additional constraints to reduce the null space of the problem.

For most problems, we do not want the filter impulse responses to vary arbitrarily, we would rather only consider filters whose impulse response varies smoothly across the output space. This preconception can be expressed mathematically by saying that, simultaneously with expression (1), we would also like to minimize

(4) |

Combining expressions (1) and (4) with a parameter, that describes their relative importance, we can write a pair of fitting goals

(5) | ||

(6) |

By making the change of variables, Fomel (1997), we obtain the following fitting goals

(7) | ||

(8) |

(9) |

4/29/2001