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气枪震源资料反褶积方法及处理流程研究
翟秋实1) 姚华建1,2) 王宝善3)
1)中国科学技术大学地球与空间科学学院,合肥市金寨路96号230026;2)蒙城地球物理国家野外科学观测研究站,安徽省亳州市233527;3)中国地震局地球物理研究所地震观测与地球物理成像重点实验室,北京100081
摘要:
气枪震源具有极高的可重复性,可用于地下介质变化的监测。但不同工作条件下气枪震源产生的信号会存在细微差异,反褶积方法能在一定程度上消除由震源变化引起的记录信号变化。为了去除气枪震源子波信号,获取气枪源到台站之间的格林函数,通常需要选取一种恰当的方法对地震波形数据进行反褶积处理。频率域水准反褶积和时间域迭代反褶积是在接收函数等领域已被广泛使用的2种反褶积方法。本文以云南宾川主动源资料为例,对比了利用这2种方法处理气枪震源资料的效果,结果表明:在计算效率方面,频率域水准反褶积方法更具优势;在处理结果的信噪比方面,时间域迭代反褶积方法表现更好,P波初至也更清晰。此外,进一步讨论了在多炮资料的处理过程中反褶积和叠加等操作的顺序问题,最后提出了从气枪震源资料中提取气枪源到台站之间的格林函数的一般流程。
关键词:  人工震源  气枪震源  反褶积  数据处理流程
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Study on the Deconvolution Method and Processing Flow of Airgun Source Data
Zhai Qiushi1) , Yao Huajian1),2) , and Wang Baoshan3)
1) School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China;2) Mengcheng National Geophysical Observatory, Bozhou 233527, Anhui, China;3) Key Laboratory of Seismic Observation and Geophysical Imaging, Institute of Geophysics, CEA, Beijing 100081, China
Abstract:
With its high repeatability, the airgun source has been used to monitor the temporal variations of subsurface structures. However, under different working conditions, there will be subtle differences in the airgun source signals. To some extent, deconvolution can eliminate changes of the recorded signals due to source variations. Generally speaking, in order to remove the airgun source wavelet signal and obtain the Green?s functions between the airgun source and stations, we need to select an appropriate method to perform the deconvolution process for seismic waveform data. Frequency domain water level deconvolution and time domain iterative deconvolution are two kinds of deconvolution methods widely used in the field of receiver functions, etc. We use the Binchuan (in Yunnan Province, China) airgun data as an example to compare the performance of these two deconvolution methods in airgun source data processing. The results indicate that frequency domain water level deconvolution is better in terms of computational efficiency; time domain iterative deconvolution is better in terms of the signal to noise ratio (SNR), and the initial motion of P-wave is also clearer. We further discuss the sequence issue of deconvolution and stack for multiple-shot airgun data processing. Finally, we propose a general processing flow for the airgun source data to extract the Green?s functions between the airgun source and stations.
Key words:  Artificial source  Airgun source  Deconvolution  Data processing flow