SEG Technical Program Expanded Abstracts 2016 2016
DOI: 10.1190/segam2016-13858493.1
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Cited by 7 publications
(4 citation statements)
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“…After removing the undesirable interferences, seismic data can be modeled as the convolution between the source wavelet w(t) and reflectivity series r(t) in the form as [10], [15], [16]: (1) where ϵ(t) is an additive random noise. Using a convolution symbol, we can rewrite it as:…”
Section: A Seismic Convolution Modelmentioning
confidence: 99%
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“…After removing the undesirable interferences, seismic data can be modeled as the convolution between the source wavelet w(t) and reflectivity series r(t) in the form as [10], [15], [16]: (1) where ϵ(t) is an additive random noise. Using a convolution symbol, we can rewrite it as:…”
Section: A Seismic Convolution Modelmentioning
confidence: 99%
“…We first test the proposed method in numerical experiments. Compared with synthetic experiments in [10] and [15], we designed the sparse reflectivity series randomly instead of using fixed positions, which makes more sense for the non-stationary seismic applications. Table I shows the comparisons of the similarity measurement between ground truth and inverted reflectivity with different SNRs.…”
Section: A Synthetic Examplementioning
confidence: 99%
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