AI 中文总结
该研究提出基于离散近似的复杂系统结构保持可视化框架，结合ARGO数据集与聚类方法，实现对全球海洋中层带垂直温盐结构的综合可解释可视化。
AI 中文摘要
本文提出一种通过离散近似构建复杂系统结构保持表示的框架，并展示其在利用ARGO数据集研究全球海洋中层带垂直温度与盐度结构中的应用。聚类作为将复杂性组织为有限结构集合的手段，这些结构可近似整体海洋状况；随后采用颜色编码设计将这些结构整合为连贯地图，三个颜色分量源自廓线可解释的几何特征：初始深度、变化幅度与形状。本文方法不聚焦于特定深度层或计算选定区域内的纬向平均，而是保留单个廓线的完整垂直结构并纳入全球海洋的每个廓线，同时捕捉精细尺度的廓线细节与大尺度空间变异性。通过对十年间收集的超一百万条廓线进行聚类，本文识别并表征了代表性廓线形状，这些形状构成了可视化策略的基础，该策略为这些海洋垂直模式的大尺度空间分布提供了综合、全面且可解释的呈现。
英文摘要
This paper presents a framework for constructing structure-preserving representations of complex systems through discrete approximation, and demonstrates its use in studying the vertical temperature and salinity structures in the mesopelagic zone across the global ocean using the ARGO dataset. Clustering serves as a means of organizing complexity into a finite set of structures that approximate the overall oceanic conditions, and a color encoding design then integrates these structures into a coherent map, with the three color components derived from interpretable geometric features of a profile: its initial level, its magnitude of variation, and its shape. Instead of focusing on specific depth levels or computing zonal averages within selected regions, our approach preserves the full vertical structure of individual profiles and incorporates each profile in the global ocean, capturing both fine-scale profile detail and large-scale spatial variability. By clustering over one million profiles collected over a decade, we identify and characterize representative profile shapes, which form the basis for a visualization strategy that provides an integrated, comprehensive, and interpretable presentation of the large-scale spatial distributions of these oceanic vertical patterns.
