Geoid and Sea Surface Topography Derived from ERS-1 Altimeter Data by the Adjoint Method

A method for splitting sea surface height measurements from satellite altimetry into geoid undulations and sea surface topography is presented. The method is based on a combination of the information from altimeter data and a dynamic sea surface height model. The model consists of geoid undulations and a quasi-geostrophic model for expressing the sea surface topography. The goal is the estimation of those values of the parameters of the sea surface height model that provide a least-squares fit of the model to the data. The solution is accomplished by the adjoint method which makes use of the adjoint model for computing the gradient of the cost function of the least-squares adjustment and an optimization algorithm for obtaining improved parameters. The estimation is applied to the North Atlantic. ERS-1 altimeter data of the year 1993 are used. The resulting geoid agrees well with the geoid of the EGM96 gravity model.     

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Pure and Applied Geophysics -     

A statistical analysis of exploration geochemical data for uranium

This paper describes a statistical analysis of reconnaissance exploration geochemical data for uranium. Three sets of data were analyzed, as they related to: (a) Charlotte-Winston-Salem and (b) Charlotte two-degree NTMS quadrangles of the south-eastern U.S.A. The coefficient of variation for uranium in each of the three sets of data was less than unity and hence no transformation of the original variable was needed. These data were subjected to correlogram analysis. A first-order Markovian model of the type: Y₀ exp (-a |p|) was fit by the least-squares method to serial correlation coefficients of these data using the method proposed by Deming (1948). The model was tested by computing the variance-volume relationship for assumed individual blocks of unit length. The noise in the input (record) was eliminated by the application of an optimum bilateral exponential smoothing technique developed by Agterberg. A comparison of spectral density estimates obtained by a maximum entropy method employing Yule-Walker equations and the Burg algorithm was also made. The prediction error coefficients needed to determine the order of the autoregressive process and hence the spectral densities were determined in both cases for the three sets of data.On leave from National Geophysical Research Institute, Hyderabad-7, India.