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复杂近表面的虚拟源成像与可重复性。

Virtual-source imaging and repeatability for complex near surface.

作者信息

Zhao Yang, Liu Tao, Tang Genyang, Zhang Houzhu, Sengupta Madhumita

机构信息

State Key Laboratory of Petroleum Resource and Prospecting, and Unconventional Petroleum Research Institute, China University of Petroleum, 18 Fuxue Road, Changping District, Beijing, 102249, China.

Sinopec Petroleum exploration & Production Research Institute, 100083, Beijing, China.

出版信息

Sci Rep. 2019 Nov 13;9(1):16656. doi: 10.1038/s41598-019-53146-w.

DOI:10.1038/s41598-019-53146-w
PMID:31723188
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6853880/
Abstract

Based on seismic interferometry, the virtual source (VS) method is able to produce virtual gathers at buried receiver locations by crosscorrelating the direct-downgoing waves with corresponding reflected-upgoing waves from surface-source gathers. Theoretically, the VS records can improve seismic quality with less negative impact from overburdened complexities. However, shallow complex structures and weathering layers at near surface not only severely distort the wavepaths, but also introduce multiples, surface waves, scattering noise, and interference among different wave modes. These additional seismic responsescontaminate both direct-downgoing and reflected-upgoing wavefields. As a result, the VS gathers experience spurious events and unbalanced illuminations associated with distorted radiation patterns. Conventional stacking operator can produce significant artifacts for sources associated with ineffective-wavepath cancellation. We review three publications and summarize a comprehensive workflow to address these issues using data-driven offset stacking, wavelet-crosscorrelation filtering, and radiation-pattern correction. A data-driven offset stacking theme, with each individual source contribution is weighted by certain quality measures, is applied for available offsets. The wavelet crosscorrelation transforms time-offset data into local time-frequency and local time-frequency-wavenumber domains. Filters are designed for the power-spectrum in each domain. The radiation-pattern correction spatially alters the contaminated direct-wavefields using a zero-phase matched filter, such that the filtered wavefield is consistent with the model-based direct P-wavefields observed at buried receiver locations. Our proposed workflow produces significant improvement as demonstrated in the 13 time-lapse field surveys that included substantial repeatability problems across a 17-month survey gap.

摘要

基于地震干涉测量法,虚拟震源(VS)方法能够通过将下行直达波与来自地表震源道集的相应上行反射波互相关来在地下检波器位置生成虚拟道集。从理论上讲,VS记录可以提高地震资料质量,且来自上覆复杂地层的负面影响较小。然而,近地表的浅层复杂结构和风化层不仅会严重扭曲波路径,还会引入多次波、面波、散射噪声以及不同波模之间的干扰。这些额外的地震响应会污染下行直达波场和上行反射波场。结果,VS道集会出现与扭曲辐射模式相关的虚假事件和不均衡照明。传统叠加算子对于与无效波路径消除相关的震源会产生明显的伪像。我们回顾了三篇文献,并总结了一个综合工作流程,以使用数据驱动的偏移叠加、子波互相关滤波和辐射模式校正来解决这些问题。对于可用偏移,应用数据驱动的偏移叠加方法,其中每个单独震源的贡献由某些质量度量加权。子波互相关将时移数据变换到局部时频和局部时频波数域。针对每个域中的功率谱设计滤波器。辐射模式校正使用零相位匹配滤波器在空间上改变受污染的直达波场,使得滤波后的波场与在地下检波器位置观测到的基于模型的直达P波场一致。如在13次时移野外测量中所展示的那样,我们提出的工作流程有显著改进,这些测量在17个月的测量间隔中存在大量重复性问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/41b0510ecf01/41598_2019_53146_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/633da709c710/41598_2019_53146_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/03d9461cbc87/41598_2019_53146_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/7ec0acd4a011/41598_2019_53146_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/72c70cb3090e/41598_2019_53146_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/5d63c1f57861/41598_2019_53146_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/271c845bdadf/41598_2019_53146_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/f5be6f41d604/41598_2019_53146_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/41b0510ecf01/41598_2019_53146_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/633da709c710/41598_2019_53146_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/03d9461cbc87/41598_2019_53146_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/7ec0acd4a011/41598_2019_53146_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/72c70cb3090e/41598_2019_53146_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/5d63c1f57861/41598_2019_53146_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/271c845bdadf/41598_2019_53146_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/f5be6f41d604/41598_2019_53146_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6753/6853880/41b0510ecf01/41598_2019_53146_Fig8_HTML.jpg

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