利用稀疏约束非平稳多项式回归去除地震噪声及拾取初至

doi:10.13810/j.cnki.issn.1000-7210.2020.03.008

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Abstract Nonstationary polynomial fitting relates to optimization with L₂ norm.Although the time-dependent characteristics of signals are considered,the residual is still assumed to be randomly distributed.In the case that there are strong non-random noises in seismic data,conventional nonstationary polynomial fitting based on L₂ norm is no longer applicable.This study investigated the theory and method of sparse-constrained nonstationary polynomial regression.First,we reviewed the basic principle of non-stationary polynomial regression.Second,to solve the problem related to the complex sparse residual,under the framework of inverse problem regularization theory,we combined non-stationary polynomial regression with L₁ norm constraint,followed the combined constraint strategy of shaping regularization with L₁ norm,and solved the multi-constraint inverse problem with conjugate gradient and projection algorithm.In addition,we estimated the coefficient of polynomial regression with time-varying smoothing characteristics and the residual sparsely distributed,which can reduce the influence of sparse strong noises on inversion.Finally,we proposed the basic process and parameter analysis of the algorithm.Synthetic and field data have proved that sparse constrained non-stationary polynomial regression is effective for noise suppression and pick up first arrival.

Key words： nonstationary polynomial regression L₂ norm L₁ norm sparse constraint pick up first arrival noise suppression

Received: 14 October 2019

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P631

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	LIU Guochang
	CAI Jiaming
	YAN Haiyang
	LI Jieli
	CHEN Xiaohong

Cite this article:

LIU Guochang,CAI Jiaming,YAN Haiyang等. Using sparse-constrained nonstationary polynomial regression to remove seismic noises and picking up first arrival[J]. OGP, 2020, 55(3): 548-556.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2020.03.008 OR http://www.ogp-cn.com/EN/Y2020/V55/I3/548

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