Full-waveform inversion of seismic data with the Neighborhood Algorithm
Full-waveform inversion of seismic data with the Neighborhood Algorithm (in Seismic inversion for reservoir properties, Reinaldo J. Michelena (prefacer), William N. Goodway (prefacer) and Tad M. Smith (prefacer))
Leading Edge (Tulsa, OK) (May 2012) 31 (5): 570-579
Stochastic (Monte Carlo) optimization methods like the Genetic Algorithm (GA) and Simulated Annealing (SA) have become increasingly popular for the inversion of geophysical data. In contrast to deterministic gradient-descent methods that search for the local minimum of the misfit function near a given starting guess, stochastic methods search for the global minimum of the misfit function even in the absence of a good starting model. Stochastic methods do not require the calculation of gradients of error surfaces. Only forward modeling is needed to evaluate the objective function. In addition to a single "best" model, some stochastic methods yield statistical information about the range of acceptable models for a given error tolerance by estimating Bayesian integrals of the posterior probability density distribution (PPD).