Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2
Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2
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DOI：
10.5194/gmd-13-3439-2020
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发表时间：
2020-07
影响因子：
5.1
通讯作者：
J. Susiluoto;Alessio Spantini;H. Haario;Teemu Härkönen;Y. Marzouk
中科院分区：
文献类型：
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作者：
J. Susiluoto;Alessio Spantini;H. Haario;Teemu Härkönen;Y. Marzouk
抽象的。卫星遥感提供了对地球上过程的全球视角，与地面测量相比具有独特的好处，例如全球覆盖和巨大的数据量。典型的缺点是空间和时间差距以及潜在的低数据质量。从这些数据中进行有意义的统计推断需要克服这些问题，并开发有效和强大的计算工具。我们设计并实现了一个计算效率高的多尺度高斯过程（GP）软件包，satGP，面向遥感应用。该软件能够处理巨大规模的问题，并计算边际和样本从随机场条件至少数亿观察。这是通过优化计算来实现的，例如，随机化并将问题分解为并行的局部子问题，这些子问题积极地丢弃无信息数据。我们描述的平均函数的高斯过程的近似边缘的马尔可夫随机场（MRF）。均值周围的变异性采用多尺度协方差内核建模，该内核由Matérn、指数和周期分量组成。我们还演示了如何风可以用来通知协方差本地。通过计算一个近似的边缘极大似然估计来学习协方差核参数，并在合成实验中验证了多尺度方法和学习核参数方法的有效性。我们将这些技术应用到一个中等大小的臭氧数据集产生的大气化学模型和从轨道碳观测2（OCO-2）卫星检索到的大量观测。SatGP软件是在开源许可证下发布的。
Abstract. Satellite remote sensing provides a global view to processes on Earth that has unique benefits compared to making measurements on the ground, such as global coverage and enormous data volume. The typical downsides are spatial and temporal gaps and potentially low data quality. Meaningful statistical inference from such data requires overcoming these problems and developing efficient and robust computational tools. We design and implement a computationally efficient multi-scale Gaussian process (GP) software package, satGP, geared towards remote sensing applications. The software is able to handle problems of enormous sizes and to compute marginals and sample from the random field conditioning on at least hundreds of millions of observations. This is achieved by optimizing the computation by, e.g., randomization and splitting the problem into parallel local subproblems which aggressively discard uninformative data. We describe the mean function of the Gaussian process by approximating marginals of a Markov random field (MRF). Variability around the mean is modeled with a multi-scale covariance kernel, which consists of Matérn, exponential, and periodic components. We also demonstrate how winds can be used to inform covariances locally. The covariance kernel parameters are learned by calculating an approximate marginal maximum likelihood estimate, and the validity of both the multi-scale approach and the method used to learn the kernel parameters is verified in synthetic experiments. We apply these techniques to a moderate size ozone data set produced by an atmospheric chemistry model and to the very large number of observations retrieved from the Orbiting Carbon Observatory 2 (OCO-2) satellite. The satGP software is released under an open-source license.