Implication of adaptive smoothness constraint and Helmert variance component estimation in seismic slip inversion

When ill-posed problems are inverted, the regularization process is equivalent to adding constraint equations or prior information from a bayesian perspective. the veracity of the constraints (or the regularization matrix r) significantly affects the solution, and a smoothness constraint is usually added in seismic slip inversions. in this paper, an adaptive smoothness constraint (asc) based on the classic laplacian smoothness constraint (lsc) is proposed. the asc not only improves the smoothness constraint, but also helps constrain the slip direction. a series of experiments are conducted in which different magnitudes of noise are imposed and different densities of observation are assumed, and the results indicated that the asc was superior to the lsc. using the proposed asc, the helmert variance component estimation method is highlighted as the best for selecting the regularization parameter compared with other methods, such as generalized cross-validation or the mean squared error criterion method. the asc also benefit other ill-posed problems in which a smoothness constraint is required.