It is therefore desirable to suppress, among all possible solutions, those that contain a potentially misleading amount of detail arising from noise amplification. Towards this goal, regularization is a standard approach that determines the most parsimonious solution of all that fit the data statistically indistinguishably well. It minimizes a measure of the information content of the solution while optimizing the quality of fit. A well-known and widely applied strategy to suppress artificial spikes is Tikhonov-Phillips regularization (Phillips 1962; Provencher 1982; Louis 1989; Hansen 1992; Press et al. 1992) , which uses, for example, the square of the second-derivative matrix (H kj ) to stabilize the solution of (4):