1. State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang, Jiangxi 330013, China
Abstract：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.
基金资助:本项研究受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）和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。
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