scholarly journals Fast quasi-harmonic weights for geometric data interpolation

2021 ◽  
Vol 40 (4) ◽  
pp. 1-15
Author(s):  
Yu Wang ◽  
Justin Solomon
Keyword(s):  
Data Interpolation ◽  
Geometric Data

scholarly journals Research on Seismic Data Interpolation and Reconstruction

2020 ◽  
Vol 1631 ◽  
pp. 012110
Author(s):  
Xiaoguo Xie ◽  
Shuling Pan ◽  
Bing Luo ◽  
Cailing Chen ◽  
Kai Chen
Keyword(s):  
Seismic Data ◽  
Data Interpolation

scholarly journals Bootstrap bounds on closed Einstein manifolds

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
James Bonifacio ◽  
Kurt Hinterbichler
Keyword(s):  
Compact Riemannian Manifold ◽  
Tree Level ◽  
Conformal Field Theories ◽  
Einstein Manifolds ◽  
Consistency Conditions ◽  
Conformal Field ◽  
Geometric Data ◽  
Conformal Bootstrap ◽  
Laplacian Operators ◽  
Kaluza Klein

Abstract A compact Riemannian manifold is associated with geometric data given by the eigenvalues of various Laplacian operators on the manifold and the triple overlap integrals of the corresponding eigenmodes. This geometric data must satisfy certain consistency conditions that follow from associativity and the completeness of eigenmodes. We show that it is possible to obtain nontrivial bounds on the geometric data of closed Einstein manifolds by using semidefinite programming to study these consistency conditions, in analogy to the conformal bootstrap bounds on conformal field theories. These bootstrap bounds translate to constraints on the tree-level masses and cubic couplings of Kaluza-Klein modes in theories with compact extra dimensions. We show that in some cases the bounds are saturated by known manifolds.

scholarly journals Focal transformation, an imaging concept for signal restoration and noise removal

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. A55-A59 ◽  
Author(s):  
A. J. Berkhout ◽  
D. J. Verschuur
Keyword(s):  
Input Data ◽  
Noise Removal ◽  
Point Of View ◽  
Data Interpolation ◽  
Signal Restoration ◽  
Physical Point ◽  
Seismic Processing ◽  
Promising Tool ◽  
Focal Area ◽  
Forward Operator

Interpolation of data beyond aliasing limits and removal of noise that occurs within the seismic bandwidth are still important problems in seismic processing. The focal transform is introduced as a promising tool in data interpolation and noise removal, allowing the incorporation of macroinformation about the involved wavefields. From a physical point of view, the principal action of the forward focal operator is removing the spatial phase of the signal content from the input data, and the inverse focal operator restores what the forward operator has removed. The strength of the method is that in the transformed domain, the focused signals at the focal area can be separated from the dispersed noise away from the focal area. Applications of particular interest in preprocessing are interpolation of missing offsets and reconstruction of signal beyond aliasing. The latter can be seen as the removal of aliasing noise.

Sign in / Sign up

Export Citation Format

Share Document