A grid-search inversion method looking for the best classification of polyphase fault/slip data

A novel, grid search-based stress inversion method is developed in this paper to find the global minima in the solution region for the classification of fault/slip data into many single-phase subsets. Exhaustively repetitious grid searches are taken to deal with possible local minima, in a departure from existing grid search-based inversion methods. Two stopping rules, to stop at the abrupt change of the objective function or at the least change of the classification, are adopted in the method to look for the best classification. Much calculation time is saved by using a modified version of conventional grid search. The feasibility of this proposed method is demonstrated by applying it to two artificial examples and two real examples. However, enormous time in calculation is still needed in the case of a data set either with a large number of data or for a large number of assigned subsets.