Convolutional quelling in seismic tomography
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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.
2016 ◽
Vol 60
(2)
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pp. 195-209
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2001 ◽
Vol 39
(2)
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pp. 233-240
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2012 ◽
Vol 38
(10)
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pp. 1759-1777
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2006 ◽
Vol 22
(1-2)
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pp. 95-112
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2009 ◽
Vol 26
(12)
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pp. 2642-2654
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2013 ◽
Vol E96.B
(2)
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pp. 569-576
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