IGGCAS OpenIR  > 油气资源研究院重点实验室
High-Efficiency Observations: Compressive Sensing and Recovery of Seismic Waveform Data
Bai, Lanshu1; Lu, Huiyi2; Liu, Yike3
2020
Source PublicationPURE AND APPLIED GEOPHYSICS
ISSN0033-4553
Volume177Issue:1Pages:469-485
AbstractWe present a new sampling scheme for seismic network observations and seismic exploration data acquisition based on compressive sensing theory. According to this theory, seismic data can be recovered with a compressive sampling scheme, using fewer samples than in traditional methods, provided that two prerequisites are met. The first prerequisite is sparse representation of the data in a transform domain. We use a one-dimensional wavelet transform to sparsely express the waveform data of the seismic network. For seismic exploration data, we use a curvelet transform as the sparse transform. The second prerequisite is incoherence between the sampling method and sparse transform. To enhance the incoherence, we propose a random sampling scheme for network and exploration observations, as random sampling is incoherent to most data transforms. In particular, we propose temporal random sampling for seismic network data observation and a full random sampling scheme in time and space for seismic exploration data. Compared with random sampling in spatial dimensions only, full random sampling further enhances incoherence because it adds the temporal dimension for randomization. Finally, seismic data are recovered from the compressive sampling data by calculating a sparsity-promoting algorithm in the sparse transform domain. We perform a real data test and synthetic data tests to illustrate that the proposed method can be used stably to achieve compressive sampling and successful recovery of high-resolution seismic waveform data. The results show that good sparse representation of the data and high incoherence between the sampling scheme and the data are important for successful recovery.
KeywordSeismic observation Seismic data compression Random sampling Compressive sensing Sparse representation
DOI10.1007/s00024-018-2070-z
WOS KeywordCONTINUOUS CURVELET TRANSFORM ; RECONSTRUCTION
Language英语
WOS Research AreaGeochemistry & Geophysics
WOS SubjectGeochemistry & Geophysics
WOS IDWOS:000519917900032
PublisherSPRINGER BASEL AG
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Document Type期刊论文
Identifierhttp://ir.iggcas.ac.cn/handle/132A11/95509
Collection油气资源研究院重点实验室
Corresponding AuthorLu, Huiyi
Affiliation1.China Earthquake Networks Ctr, Beijing 100045, Peoples R China
2.Kerogen Energy Serv Co Ltd, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China
Recommended Citation
GB/T 7714
Bai, Lanshu,Lu, Huiyi,Liu, Yike. High-Efficiency Observations: Compressive Sensing and Recovery of Seismic Waveform Data[J]. PURE AND APPLIED GEOPHYSICS,2020,177(1):469-485.
APA Bai, Lanshu,Lu, Huiyi,&Liu, Yike.(2020).High-Efficiency Observations: Compressive Sensing and Recovery of Seismic Waveform Data.PURE AND APPLIED GEOPHYSICS,177(1),469-485.
MLA Bai, Lanshu,et al."High-Efficiency Observations: Compressive Sensing and Recovery of Seismic Waveform Data".PURE AND APPLIED GEOPHYSICS 177.1(2020):469-485.
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