SearchSkip to Search Results
- 1AVO inversion
- 1Approximate Value/Policy Iteration
- 1Bayesian inversion
- 1Bivariate Cauchy
We study penalized fitting strategies aimed at sparse model selection of models satisfying certain hierarchical restrictions, in linear models arising from factorial experiments. After discussing various merits of existing approaches, we propose a modification and generalization of the approach...
In this thesis, we study penalized methods in time series and functional data analysis. In the first part, we introduce regularized periodograms for spectral analysis of unevenly spaced time series. The regularized periodograms, called regularized least squares periodogram and regularized...
This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and finite action space. We follow the value-based approach in which a function approximator is used to estimate the optimal value function....
Regularization of the AVO inverse problem by means of a multivariate Cauchy probability distributionDownload
Amplitude Variation with O set (AVO) inversion is one of the techniques that is being used to estimate subsurface physical parameters such as P-wave velocity, S-wave velocity, and density or their attributes. AVO inversion is an ill-conditioned problem which has to be regularized in order to...
Vector interpolation and regularized elastic imaging of multicomponent seismic data
Historically seismic data processing has relied on the acoustic approximation to process single component data under the simplifying assumption that the recorded wavefield consists mainly of compressional wave modes. With the advancement of multicomponent seismic technology there is an increased...