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基于tau-p域相干控制束偏移方法

The Coherent of Controlled Beam Migration in Tau-p Domain
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摘要 在实际地震资料数据中,存在一定的随机噪音和低信噪比的干扰信号,高斯射线束偏移方法对T-p域坐标图板上所有的能量进行偏移成像,因此导致其偏移成像存在随机噪音情况,干扰了地下构造成像质量。基于T-p域相干性控制束偏移是本文在高斯束偏移方法的基础上,利用了在T-p倾斜叠加原理图进行的能量相干加权提出的成像方法.该方法采用倾斜叠加前后相干值对比策略,根据倾斜叠加前与沿着t=T+pX进行倾斜叠加能量加权相干后将地震波束的有效能量约束在一定的范围內,倾斜叠加原理图上那些相对振幅较强的样点,意味着为地下同相轴倾斜叠加强点,再经过相干加权,能大大的压制地下随机噪音等,能充分的改善复杂构造区域的成像质量,也通过Marmousi模型引入不同的低信噪比数据和某探区的实际资料进行测试来验证这种方法的正确性和有效性. In the in-situ seismic data, there are certain random noise and low signal-to-noise ratio interference signals. The imaging method of Gaussian beam migration takes advantage of all the energy in the tau-p domain coordinate map, thus causing certain random noise in it, which interferes with the quality of underground structure imaging. On the basis of the method of Gaussian beam migration, a new method based on the coherent of controlled beam in the tau-p domain is developed in the present study, which makes full use of the coherent of the effective energy in the local slant-stack principle. In this method, the compare between before and after in the local slant-stack principle of the coherent value. The effective energy of the seismic beam is constrained to a certain range according to the slope of t=T+px by local slant-stack principle of the coherent value. The samples with strong relative amplitude on the map of local slant-stack representing the strong points of the event in underground, then the coherent weighting can greatly suppress the underground random noise, which can greatly improve the imaging quality of the complex structure area. By employing the Marmousi model with the different low signal-to-noise ratio, and the actual data of a certain exploration area were tested to verify the correctness and effectiveness of the new method.
作者 吴建文 陈洁鉴 肖波 WU Jian-wen;CHEN Jie-jian;XIAO Bo(Guangzhou Marine Geological Survey,Guangzhou 510075,China)
出处 《海洋信息》 2019年第3期43-49,共7页 Marine Information
关键词 T-p域 倾斜叠加 能量加权相干 低信噪比数据 tau-p domain local slant-stack coherent of effective energy low signal to noise ratio data
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