首页|基于高阶TV正则化的叠前动校正域随机噪声压制方法

基于高阶TV正则化的叠前动校正域随机噪声压制方法

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  • 万方数据
  • 维普
常规全变分(Total Variation,TV)去噪模型只考虑水平方向和垂直方向的一阶导数信息,处理存在弯曲同相轴的叠前地震资料时会严重破坏振幅信息,而且振幅的横向渐变特征会被压制,从而引起"阶梯效应".常利用地震数据的局部倾角信息提高TV模型的保幅能力,但局部倾角信息的计算会受到噪声的严重影响.为此,提出在动校正(NMO)域中利用高阶TV正则化去噪模型对叠前地震资料进行随机噪声压制.该方法首先将叠前地震数据转换到NMO域,NMO对噪声的鲁棒性强,同时避免了局部倾角的计算;在NMO域中弯曲同相轴被拉平,然后对其进行高阶TV去噪;最后通过反NMO还原叠前地震数据.以二阶导数为例构造了高阶TV正则化反演去噪目标函数,并在分裂Bregman优化框架下推导了快速优化求解方法.合成地震数据和实际地震资料的处理结果表明,该方法不仅可以有效压制随机噪声,而且可以消除同相轴弯曲和"阶梯效应"造成的振幅失真,提高了 TV去噪方法的保幅性能.
Random noise suppression method in prestack NMO domain based on high-order TV regularization
The conventional total variation(TV)regularization model only considers the first-order derivative information in the horizontal and vertical directions.When dealing with prestack seismic data with curved reflec-tion events,it can severely damage the amplitude information and cause"staircase effects"by suppressing the lateral gradient characteristics of the amplitude.The local dip information of seismic data is often applied to im-prove the amplitude-preserving ability of the TV model.However,the calculation of local dip information itself will be impacted by noise.To address this issue,this paper proposes a high-order TV regularization model to suppress random noise in prestack seismic data in the domain of normal moveout(NMO).This method first transforms the prestack seismic data into the NMO domain,NMO is robust to noise and avoids the calculation of the local dip angle.In the NMO domain,the curved event is flattened,and then high-order TV denoising is per-formed.Finally,the prestack seismic data are restored through inverse NMO.Taking the second-order deriva-tive as an example,a high-order TV regularization inversion denoising objective function is constructed,and a fast optimization method is derived under the split Bregman optimization framework.The processing results of synthetic seismic data and actual seismic data show that this method can not only effectively suppress random noise but also eliminate amplitude distortion caused by curved reflection events and"staircase effects",im-proving the amplitude preservation performance of the TV denoising method.

high order TV regularizationnormal moveout(NMO)domainrandom noise suppressionampli-tude-preserving denoisingsplit Bregman optimization framework

张鹏、郝亚炬、朱云峰、张红静、殷铎文、田宵

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东华理工大学核资源与环境国家重点实验室,江西南昌 330013

东华理工大学地球物理与测控技术学院,江西南昌 330013

高阶TV正则化 动校正(NMO)域 随机噪声 保幅去噪 分裂Bregman优化框架

国家自然科学基金项目江西省自然科学基金项目&&江西省教育厅科学技术研究项目核资源与环境国家重点实验室开放基金项目&&东华理工大学研究生创新专项资金项目

4200411420202BAB21101020224BAB213047GJJ22007462020NRE272022NRE16DHYC-202314

2024

10.13810/j.cnki.issn.1000-7210.2024.01.006

石油地球物理勘探
东方地球物理勘探有限责任公司

石油地球物理勘探

CSTPCD北大核心
影响因子：1.766
ISSN：1000-7210
年,卷(期)：2024.59(1)
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