An Ocean Acoustic Modeling Based on the Physical Parameters Clustering of the Arctic Ice Floes: Applied to the Study of Acoustic Field Response as Ice Floes Thickness Varies

Abstract

Abstract The varies of ice floe thickness (IFH) is a very influential research content in the Earth system academic research, it is closely related to the distribution of underwater acoustic fields. However, the discontinuous physical parameters of ice floes make it difficult to clear this relationship with conventional acoustic propagation models or measured data. Unlike the conventional acoustic models that treat ice floes as one or more physical simplified acoustic reflection layers, this study employs machine learning and FEM (finite element method) modeling to incorporate ice floes' physical parameters into the acoustic field model. The physical parameters of ice floes associated with acoustic field modeling are screened by using the principal component analysis method. On this basis, these ice floes were spatially divided into three highly differentiated and interpretable typical ice clusters using the K‐Means method. We treated ice as a multi‐layer elastic medium, and then the FEM acoustic model was built under the fluid‐structure coupling condition. Measured data (UNDER‐ICE) and MIT acoustic models (OASES) were used to validate our FEM results. We demonstrated that the modeling improves the distribution characteristics of surface channel arrival amplitude under ice floes cover. From the perspective of the model application, the acoustic field response as IFH varies is closely related to the category and region of ice floes.