一种改进型seislet域迭代阈值压制混叠噪声方法

doi:10.11720/wtyht.2020.1097

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Abstract

High productivity acquisition technology can improve the efficiency of seismic acquisition.However,the existence of blended noise between adjacent sources severely decreases the signal to noise (S/N) ratio of seismic data and limits the application in the field.To solve the issue,an improved blended noise suppression approach is proposed by combining NMO,median filter with the seislet transform based threshold de-noising.Firstly,median filter is applied to the original blended data with NMO correction.Next,we continue to extract the residual energy to get the de-blended result by the seislet transform based threshold denoising.Then,re-process the residual data by subtracting the original pseudo de-blended data and the pseudo de-blended data of the de-blended result from each iteration as the above processing flow.Finally,the final de-blended data is obtained by adding the remained energy of each iteration.Simulated data tests demonstrate the proposed approach can protect effective signal while suppressing the blended noise.

Key words： high productivity acquisition blended noise seislet domain median filter pseudo de-blended iterative threshold

Received: 26 February 2019 Published: 24 June 2020

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	Articles by authors
	Lie-Qian DONG
	Heng ZHOU
	Shan-Li GUO
	Lian-Bin JIANG
	Zhong JIANG
	Wen-Jie YU

Cite this article:

Lie-Qian DONG,Heng ZHOU,Shan-Li GUO, et al. An optimized blending noise suppression based on seislet domain iterative threshold denoising approach[J]. Geophysical and Geochemical Exploration, 2020, 44(3): 568-572.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2020.1097 OR https://www.wutanyuhuatan.com/EN/Y2020/V44/I3/568

Optimized blended noise suppression workflow based on seislet domain iterative threshold method

Synthetic data
a—unblended data;b—simulated blended data

Deblending contrast of simulated synthetic data
a、b—the de-blended result by the F-K domain threshold approach and the separated blended noise;c、d—the de-blended result by the seislet domain threshold approach and the separated blended noise;e、f—the de-blended result by the proposed approach and the separated blended noise

Filed data
a—unblended data;b—simulated blended data

Deblending contrast of field data
a、b—the de-blended result by the F-K domain threshold approach and the separated blended noise;c、d—the de-blended result by the seislet domain threshold approach and the separated blended noise;e、f—the de-blended result by the proposed approach and the separated blended noise

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