Comparison of outlier detection algorithms for GOCE gravity gradients
goce will be the first satellite ever to measure the second derivatives of the Earth’s gravitational potential in space. It will be possible to derive a high accuracy and high resolution model of the gravitational field if systematic errors and/or outliers have been removed from the data. It is necessary to detect outliers in the data pre-processing because undetected outliers may lead to erroneous results when the data are further processed, for example in the recovery of a gravity field model. Outliers in the goce gravity gradients will be searched for and detected in the gravity field analysis pre-processing step.
In this paper, a number of algorithms are discussed that detect outliers in the diagonal gravity gradients. One of them combines wavelets with either a statistical method or filtered gradients with an identification rate of about 90% or more. Another high performing algorithm is the combination of three methods, that is, the tracelessness condition (a physical property of the diagonal gradients), comparison with model or filtered gradients, and along-track interpolation of gradient anomalies. Using two sets of simulated gravity gradients, the algorithms are compared in terms of their identification rate and number of falsly detected outliers. In addition, it is shown that the quality of the gravity field solution is very much affected by outliers. Undetected outliers can degrade the gravity field solution by up to twenty times as compared with a solution without outliers.
KeywordsOutliers goce mission Gradiometry Statistical tests
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