论文信息 - Comparison of outlier detection algorithms for GOCE gravity gradients

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.

[1] N. K. Pavlis,et al. The Development of the Joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) Geopotential Model EGM96 , 1998 .

[2] Jim Freeman,et al. Outliers in Statistical Data (3rd edition) , 1995 .

[3] Richard H. Rapp,et al. The Ohio State 1991 geopotential and sea surface topography harmonic coefficient models , 1991 .

[4] Carsten Mayer,et al. Wavelet Modelling of Ionospheric Currents and Induced Magnetic Fields From Satellite Data , 2003 .

[5] R. Koop,et al. Calibration of GOCE SGG data using high–low SST, terrestrial gravity data and global gravity field models , 2001 .

[6] Carl Christian Tscherning,et al. Calibration of satellite gradiometer data aided by ground gravity data , 1998 .

[7] Roland Pail,et al. Outlier detection algorithms and their performance in GOCE gravity field processing , 2005 .

[8] J Bouman,et al. >Quick-look outlier detection for GOCE gravity gradients , 2004 .

[9] C. Readings,et al. Gravity field and steady-state ocean circulation mission , 1996 .

[10] Roland Pail,et al. GOCE Quick-Look Gravity Solution: Application of the Semianalytic Approach in the Case of Data Gaps and Non-Repeat Orbits , 2003 .

[11] Vic Barnett,et al. Outliers in Statistical Data , 1980 .

[12] C. Tscherning. THE USE OF OPTIMAL ESTIMATION FOR GROSS ERROR DETECTION IN DATABASES OF SPATIALLY CORRELATED DATA , 2023, Proceeding of Data For Discovery.