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Abstract

Consider a system in which the input is described by a set of M parameters m, and the prescribed output of the system by a linear transform on m, A m, where A is an M × N matrix. The objective is to minimize the difference, specified by the 2-normA m − d2, between the expected output from the system and the observed data d (e.g. grey scale values for each cell in a rasterized image). The estimated (least squares) solution is given by: \( {\boldsymbol{m}}_{est}=\frac{1}{{\mathbf{A}}^{\mathrm{T}}\mathbf{A}}{\mathbf{A}}^{\mathrm{T}}\boldsymbol{d} \), where T represents matrix transposition. However, because in practice, real data contains errors, the matrix A is often ill-conditioned, and an optimum solution has to be sought for using the technique of regularization. The objective function is:

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Howarth, R.J. (2017). L. In: Dictionary of Mathematical Geosciences . Springer, Cham. https://doi.org/10.1007/978-3-319-57315-1_12

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