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Abstract

Summary

The similarity-weighted stacking takes use of the local similarity between each trace and a reference as the weight to stack the NMO-corrected prestack seismic data. The selection of reference trace plays a significant role in the final performance. The traditional similarity-weighted stacking approach uses the traditionally stacked trace as the reference, which is not plausible when there is abnormal trace in the data. We proposed a new criteria for selecting the reference trace, which is based on the stacked trace after applying a truncated singular value decomposition to the NMO corrected data. The TSVD aims to reduce the impact of the abnormal trace in the stacked trace, and thus can make the similarity-weighted stacking more robust. We use both synthetic and field data examples to show the successful performance of the proposed approach.

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/content/papers/10.3997/2214-4609.201601325
2016-05-30
2024-04-20

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Stacking Using Truncated Singular Value Decomposition and Local Similarity
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201601325
/content/papers/10.3997/2214-4609.201601325
/content/papers/10.3997/2214-4609.201601325
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