论文标题
重力梯度张量成分及其组合的随机不确定性分析
Stochastic uncertainty analysis of gravity gradient tensor components and their combinations
论文作者
论文摘要
全张量重力（FTG）设备可提供多达五个重力梯度张量的独立组件。但是，我们尚无定量了解哪些张量成分或组件组合对于通过重力反演恢复地下密度模型更为重要。这主要是因为在不同的情况或目的中，不同的组件可能更合适。在不同环境中了解这些组件的知识可以帮助选择组件组合的最佳选择。在这项工作中，我们建议应用随机倒置以评估重力梯度张量成分及其组合的不确定性。因此，该方法是一种定量方法。此处的应用方法基于使用Cokriging的地理倒置（高斯过程回归）概念。发现Cokriging方差（GP的方差函数）是区分重力梯度张量分量的有用指标。这种方法应用于新发现的数据集，以证明其在现实世界应用中的有效性。
Full tensor gravity (FTG) devices provide up to five independent components of the gravity gradient tensor. However, we do not yet have a quantitative understanding of which tensor components or combinations of components are more important to recover a subsurface density model by gravity inversion. This is mainly because different components may be more appropriate in different scenarios or purposes. Knowledge of these components in different environments can aid with selection of optimal selection of component combinations. In this work, we propose to apply stochastic inversion to assess the uncertainty of gravity gradient tensor components and their combinations. The method is therefore a quantitative approach. The applied method here is based on the geostatistical inversion (Gaussian process regression) concept using cokriging. The cokriging variances (variance function of the GP) are found to be a useful indicator for distinguishing the gravity gradient tensor components. This approach is applied to the New Found dataset to demonstrate its effectiveness in real-world applications.
