Direct Mean Strain Estimation for Elastography Using Nearest-Neighbor Weighted Least-Squares Approach in the Frequency Domain

2012 ◽  
Vol 38 (10) ◽  
pp. 1759-1777 ◽  
Author(s):  
Md. Kamrul Hasan ◽  
Emran Mohammad Abu Anas ◽  
S. Kaisar Alam ◽  
Soo Yeol Lee
Keyword(s):  
Least Squares ◽  
Frequency Domain ◽  
Nearest Neighbor ◽  
Weighted Least Squares ◽  
Strain Estimation ◽  
Mean Strain ◽  
Least Squares Approach

Convolutional quelling in seismic tomography

Geophysics ◽  
1989 ◽  
Vol 54 (5) ◽  
pp. 570-580 ◽  
Author(s):  
Keith A. Meyerholtz ◽  
Gary L. Pavlis ◽  
Sally A. Szpakowski
Keyword(s):  
Least Squares ◽  
Seismic Tomography ◽  
Nearest Neighbor ◽  
Sparse Matrix ◽  
Weighted Least Squares ◽  
Synthetic Data ◽  
Least Squares Solution ◽  
Smoothing Filter ◽  
Imaging Artifacts ◽  
Tomography Data

This paper introduces convolutional quelling as a technique to improve imaging of seismic tomography data. We show the result amounts to a special type of damped, weighted, least‐squares solution. This insight allows us to implement the technique in a practical manner using a sparse matrix, conjugate gradient equation solver. We applied the algorithm to synthetic data using an eight nearest‐neighbor smoothing filter for the quelling. The results were found to be superior to a simple, least‐squares solution because convolutional quelling suppresses side bands in the resolving function that lead to imaging artifacts.

Sign in / Sign up

Export Citation Format

Share Document