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Multiple subtraction using statistically estimated inverse wavelets
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DOI：10.1190/1.3494082.png)
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Surface-related multiple elimination (SRME) typically consists of two steps: The first step is prediction and the second step is subtraction. In subtraction, it is important to effectively attenuate multiple events and preserve primary events. When multiples cross with or overlap on primaries, least-square subtraction usually cannot subtract multiples effectively and may also damage the primaries. When multiples overlap with primaries, least-square subtraction cannot always subtract multiples accurately and often damages the primaries. To remedy this problem, we propose to statistically estimate the inverse source wavelet, correct for errors in the estimate of the inverse wavelet, and then use the corrected inverse wavelets for multiple subtraction. Synthetic tests and real data examples show that the proposed method can effectively attenuate multiples, while they also preserve the continuity of reflection events and successfully avoid amplitude distortion. The proposed method is characterized by low computational costs and ease of implementation.
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