Efficient environmental uncertainty propagation using the probabilistic collocation method

Uncertainty in ocean environmental parameters is often the dominant source of uncertainty in an underwater acoustic field calculation. In addition, the relationship between environmental parameters and the resulting field prediction may be highly nonlinear, leading to the explicit determination of the resulting field uncertainty uneasy. The direct Monte Carlo (MC) sampling method which is conventionally used to compute the field uncertainty is computationally prohibitive. To overcome the drawbacks of the existing methods, this paper proposed a Probabilistic Collocation Method (PCM) based method to propagate the uncertainty efficiently. The basic concept of the method is to represent the random acoustic field by Polynomial Chaos Expansions (PCE) and to calculate the coefficients in the PCE using PCM. Statistical properties can then be obtained from the PCE easily. Computer simulations show that PCM can propagate the uncertainty efficiently when the distribution of the result acoustic is unimodal.