Applying the multivariate Gaidai reliability method in combination with an efficient deconvolution scheme to prediction of extreme ocean wave heights

Current study applies novel Gaidai risks assessment approach that, may be utilized for wave heights spatiotemporal analysis, hence advancing studies of the effects of climate change on the planet. Gaidai risks assessment approach is particularly fit for multivariate environmental dynamic systems, that are either numerically MC (Monte Carlo) simulated, or physically observed across representative period of time, to produce coherent ergodic time-series. Nowadays, offshore ocean waves being crucial to operational safety and reliable production of offshore conventional (oil and gas) and renewable energy. The 1st advocated reliability spatiotemporal technique, being suitable for high-dimensional dynamic systems, represented by quasi-ergodic time-series. The second advocated reliability technique, being suitable for unidimensional extreme value predictions, and may be well applied to a wide range of design and engineering applications. In case of in-situ environmental loads, an accurate forecast of environmental system’s failure/hazard/damage likelihood is also attainable, as has been demonstrated in this study. Moreover, classic risks assessment methods, dealing with time-series do not always possess advantages of dealing easily with environmental dynamic system’s multi-dimensionality, having nonlinear cross-correlations between different environmental system’s critical dimensions/components. By applying advocated deconvolution methodology to in-situ significant wave-height dataset, measured in the offshore area, close to Norwegian Heidrun oil field, this study seeks to demonstrate advocated method’s effectiveness and accuracy. It is widely known that offshore waves represent quite complex environmental highly-nonlinear, multidimensional yet cross-correlated environmental dynamic system. Global warming, along with climate change being an important factor, affecting increased ocean wave-heights extremes. The purpose of current study has been to benchmark the novel risks assessment methodology, enabling statistically optimal usage of the underlying raw dataset, extracted from the measured time-histories. The method put forth in this study may be used for failure/damage/hazard risk assessments for nonlinear high-dimensional environmental dynamic systems as a whole, in an easy, yet effective manner.