A profile from the Argo ocean observation array is a sequence of three‐dimensional vectors composed of pressure, salinity, and temperature, appearing as a continuous curve in three‐dimensional space. The shape of this curve is faithfully represented by a path signature, which is a collection of all the iterated integrals. Moreover, the product of two terms of the signature of a path can be expressed as the sum of higher‐order terms. As a result of this algebraic property, a nonlinear function of the profile shape can always be represented by a weighted linear combination of the iterated integrals, which enables machine learning of a complicated function of the profile shape. In this study, we performed supervised learning for existing Argo data with quality control flags by using the signature method and demonstrated the estimation performance by cross validation. Unlike rule‐based approaches, which require several complicated and possibly subjective rules, this method is simple and objective in nature because it relies only on past knowledge regarding the shape of profiles. This technique is critical for realizing automatic quality control for Argo profile data.

中文翻译：

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使用签名方法的机器学习技术用于Argo型材的自动质量控制

来自Argo海洋观测阵列的剖面是由压力，盐度和温度组成的三维矢量序列，在三维空间中显示为连续曲线。该曲线的形状如实地由路径签名表示，该路径签名是所有迭代积分的集合。此外，路径签名的两个项的乘积可以表示为高阶项的总和。由于这种代数性质，轮廓形状的非线性函数始终可以通过迭代积分的加权线性组合来表示，这使得可以对轮廓形状的复杂函数进行机器学习。在这个研究中，我们使用签名方法对带有质量控制标志的现有Argo数据进行了监督学习，并通过交叉验证证明了估计性能。与基于规则的方法（它需要多个复杂且可能是主观的规则）不同，该方法本质上简单而客观，因为它仅依赖于有关轮廓形状的以往知识。该技术对于实现Argo配置文件数据的自动质量控制至关重要。