A SIMULATED ANNEALING APPROACH TO SEISMIC MODEL OPTIMIZATION WITH SPARSE PRIOR INFORMATION

It is well known that seismic inversion based on local model optimization methods, such as iterative use of linear optimization, may fail when prior information is sparse. Where the seismic events corresponding to reflectors of interest remain to be identified, a global optimization technique is required. We investigate the use of a global, stochastic optimization method, that of simulated annealing, to solve the seismic trace inversion problem, in which the two-way traveltimes and reflection coefficients are to be determined. The simulated annealing method is based on an analogy between the model-algorithm system and a statistical mechanical system. We exploit this analogy to produce improved annealing schedules. It is shown that even in cases of virtually no prior information about two-way traveltimes and reflection coefficients, the method is capable of producing reliable results.