论文详情
在压缩感知采样理论下开展“两宽一高”高效地震数据采集,在可控的投资成本下能获得更高质量的成像结果。提出了随机采样观测系统设计的基本原则,即在高密度地震数据采集(或常规地震数据采集)观测系统建立的基础网格的基础上,按照高斯随机采样的理论要求,将规则欠采样的网格作为高斯随机采样位置的期望,分别在炮集范围进行空间随机检波点位置的设计和在整个工区进行空间随机炮点位置的设计。以特征波场(初至波场或标志性的反射波场)为随机观测系统感知的对象,用频率域地震数据Hankel矩阵的低秩特性作为稀疏性的度量标准,通过生成符合高斯分布的随机观测系统,测试随机采样加稀疏提升算法对于恢复无假频的地震数据能力及其影响因素。数值实验结果表明,随机采样加压缩感知数据重建后,可以较好地恢复密网格采样数据。同时,相对于随机采样和规则采样的地震数据,重建数据的偏移成像质量都有所提升。实际应用中,若考虑近地表散射噪声和静校正量等因素,则对随机观测系统的设计及数据重建算法提出了更高的要求。
The efficient acquisition of “broadband,wide-azimuth,and high density” data under compressed sensing (CS) theory can help to obtain higher quality imaging results without increasing the cost.A randomized seismic acquisition technology is proposed herein.First,a regular grid for the high-density (or typical) acquisition was defined.Afterwards,based on Gaussian sampling theory,randomized positions of sources and receivers were established,for which a regular,under-sampled grid as the expectation of Gaussian random sampling position.The supposed characteristic wavefields (i.e.first-arrival wavefields or specified reflections) were selected as the CS data,and the low-rank feature of the Hankel matrix composed by the frequency-domain seismic data could be used to characterize the sparsity.The abilities of random sampling and a sparse lifting algorithm to recover seismic data without false frequency and its influencing factors were tested by generating a Gaussian randomized acquisition.Numerical examples demonstrated that the dense sampling data can be recovered using random sampling and CS.Moreover,the seismic image of the recovered data was better than those of the randomly or even regularly sampled seismic data.In practice,the near-surface seismic scattering and the statics increase the complexity of seismic data,which makes the design of the randomized seismic acquisition and the data reconstruction more challenging.
南方海洋科学与工程广东省实验室(湛江)资助项目(ZJW-2019-04),中国石化地球物理重点实验室(33550006-19-FW0399-0041),国家重点研发计划变革性技术关键科学问题重点专项(2018YFA0702503)、国家重点研发计划深海关键技术与装备重点专项(2019YFC0312004)、国家自然科学基金(41704111,41774126)、国家科技重大专项(2016ZX05024-001,2016ZX05006-002)和国家科技重大专项“海相碳酸盐岩地震勘探关键技术”(2017ZX05005-004)的共同资助。