基于高阶TV正则化的叠前动校正域随机噪声压制方法  

Random noise suppression method in prestack NMO domain based on high⁃order TV regularization

作　　者：张鹏 郝亚炬 朱云峰 张红静 殷铎文 田宵 ZHANG Peng;HAO Yaju;ZHU Yunfeng;ZHANG Hongjing;YIN Duowen;TIAN Xiao(State Key Laboratory of Nuclear Resources and Environment,East China University of Technology,Nanchang,Jiangxi 330013,China;School of Geophysics and Measurement-Control Technology,East China University of Technology,Nanchang,Jiangxi 330013,China)

机构地区：[1]东华理工大学核资源与环境国家重点实验室,江西南昌330013 [2]东华理工大学地球物理与测控技术学院,江西南昌330013

出　　处：《石油地球物理勘探》2024年第1期70-79,共10页Oil Geophysical Prospecting

基　　金：国家自然科学基金项目“组稀疏结构和等效衰减模型双重约束下的叠前Q值反演方法研究”(42004114);江西省自然科学基金项目“基于压缩感知的地震数据自适应压缩及反射系数快速反演”(20202BAB211010);“基于人工智能的江西地区天然地震和非天然地震事件识别方法研究”(20224BAB213047);江西省教育厅科学技术研究项目“定向高阶导数TV正则化保幅地震噪声压制算法研究”(GJJ2200746);核资源与环境国家重点实验室开放基金项目“致密层系井震结合计算三维TOC实现油铀兼探方法研究”(2020NRE27);“井震结合下基于谱蓝化—有色反演的松辽盆地南部姚家组砂岩型铀矿预测方法研究”(2022NRE16);东华理工大学研究生创新专项资金项目“基于同相轴拉平技术的高阶TV正则化地震资料保幅去噪算法研究”(DHYC‑202314)联合资助。

摘　　要：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。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 reflection 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 improve 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 performed.Finally,the prestack seismic data are restored through inverse NMO.Taking the second‑order derivative 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”,improving the amplitude preservation performance of the TV denoising method.

关 键 词：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 

分 类 号：P631[天文地球—地质矿产勘探]