Abstract

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).

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