Convolutional quelling in seismic tomography

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.