Data Interpolation

2020 ◽  
pp. 105-124
Author(s):  
Eihab B. M. Bashier
Keyword(s):  
Data Interpolation

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 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.

Study on Lidar Data Interpolation Method Based on GA-BP

2012 ◽  
Vol 588-589 ◽  
pp. 1312-1315
Author(s):  
Yi Kun Zhang ◽  
Ming Hui Zhang ◽  
Xin Hong Hei ◽  
Deng Xin Hua ◽  
Hao Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Lidar Data ◽  
Data Interpolation ◽  
Interpolation Model ◽  
Genetic Method ◽  
Interpolation Accuracy ◽  
Linear Interpolation Method

Aiming at building a Lidar data interpolation model, this paper designs and implements a GA-BP interpolation method. The proposed method uses genetic method to optimize BP neural network, which greatly improves the calculation accuracy and convergence rate of BP neural network. Experimental results show that the proposed method has a higher interpolation accuracy compared with BP neural network as well as linear interpolation method.

scholarly journals Error estimates for scattered data interpolation on spheres

1999 ◽  
Vol 68 (226) ◽  
pp. 733-748 ◽  
Author(s):  
Kurt Jetter ◽  
Joachim Stöckler ◽  
Joseph D. Ward
Keyword(s):  
Error Estimates ◽  
Scattered Data ◽  
Data Interpolation ◽  
Scattered Data Interpolation
Sign in / Sign up

Export Citation Format

Share Document