Year of publication
- 2000 (3) (remove)
- Spherical Tikhonov Regularization Wavelets in Satellite Gravity Gradiometry with Random Noise (2000)
- This paper considers a special class of regularization methods for satellite gravity gradiometry based on Tikhonov spherical regularization wavelets with particular emphasis on the case of data blurred by random noise. A convergence rate is proved for the regularized solution, and a method is discussed for choosing the regularization level a posteriori from the gradiometer data.
- Multiscale Signal-to-Noise Thresholding (2000)
- The basic idea behind selective multiscale reconstruction of functions from error-affected data is outlined on the sphere. The selective reconstruction mechanism is based on the premise that multiscale approximation can be well-represented in terms of only a relatively small number of expansion coefficients at various resolution levels. An attempt is made within a tree algorithm (pyramid scheme) to remove the noise component from each scale coefficient using a priori statistical information (provided by an error covariance kernel of a Gaussian, stationary stochastic model).