Uncertainty quantification of soil properties in offshore pile-driving noise predictions with the air-bubble curtain system

Abstract

Offshore wind turbines supply a significant source of sustainable energy. Installation of foundation piles in offshore wind leads to underwater noise emissions, which can harm the marine ecosystem. Although several noise control strategies exist to reduce the sound levels to within acceptable limits, the air-bubble curtain system is one of the most widely applied sound mitigation systems. Modeling the underwater noise emissions is quite challenging due to the large uncertainty in the identification of the dynamic properties of the marine sediment over a wide frequency range. In this paper, a probabilistic framework is adopted to determine the best-fit probability distributions of the soil variables. A copula-based multivariate probabilistic model is then used to analyze the dependencies between multiple soil variables. The developed probabilistic framework is integrated to an existing computational model for the noise prediction due to impact piling which includes the noise reduction module of an air bubble curtain. A case study is discussed in which predicted sound levels are utilized to identify correlations between seabed properties and noise levels. Given the large uncertainty in the soil characterization, a systematic approach is proposed to quantify the performance of the air-bubble curtain.