Seismic Data Regularization on Nonequispaced Grid via a Joint Sparsity-Promotion Method
Seismic Data Regularization on Nonequispaced Grid via a Joint Sparsity-Promotion Method
复制标题
通过联合稀疏度提升方法对非等距网格地震数据进行正则化
DOI：
10.1109/lgrs.2021.3072356
复制
发表时间：
2022
影响因子：
4.8
通讯作者：
Yu Siwei
中科院分区：
文献类型：
--
作者：
Wang Xiaojing;Yu Siwei
地震数据正则化问题是地震数据处理的关键问题。提出了一种基于压缩感知理论的联合稀疏性提升方法——基于曲线数据驱动的紧密框架稀疏性提升（CDSP）方法。CDSP方法直接在非均匀网格上沿空间维度对地震数据进行正则化。在投影规则空间网格上，同时利用曲线和数据驱动的紧框架变换的联合稀疏性。通过离散化傅里叶变换实现非均匀网格到均匀网格的投影。与基于曲线稀疏提升（curvellet -sparsity-promotion， CSP）的正则化方法相比，CDSP将预定义曲线变换和自适应学习稀疏变换的优点结合到一个优化模型中。采用一种可选方向乘法器（ADMM）来求解优化问题。一个综合算例和两个现场算例表明，CDSP方法比CSP方法更能保持事件的连续性，产生的伪影更少。
The seismic data regularization problem is vital to seismic data processing. We propose a joint sparsity-promotion method based on the compressive sensing theory named the curvelet-data-driven-tight-frame-based sparsity-promoting (CDSP) method. The CDSP method regularizes the seismic data directly on the nonequispaced grid along the spatial dimension. The joint sparsity is exploited in the curvelet and data-driven tight frame transform simultaneously on the projected regular spatial grid. The projection from the nonequispaced grid to the equispaced grid is achieved by the nonequispaced discretized Fourier transform. Comparing with the curvelet-sparsity-promotion-based (CSP) regularization method, CDSP combines the advantage of the predefined curvelet transform and adaptive-learned sparse transform into one single optimization model. An alternative directional method of multipliers (ADMM) is applied to solve the optimization problem. One synthetic and two field examples show that the CDSP method performs better on the preservation of the continuity of events and produces less artifacts than the CSP method.