Making the Most out of the Least (Squares Migration)
Making the Most out of the Least (Squares Migration)
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DOI：
10.1190/segam2014-1242.1
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
Gaurav Dutta;Yunsong Huang;W. Dai;Xin Wang;G. Schuster
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文献类型：
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作者：
Gaurav Dutta;Yunsong Huang;W. Dai;Xin Wang;G. Schuster
标准偏移图像可能由于以下原因而遭受偏移伪影：1）差的源-接收器采样，2）由几何扩展引起的弱振幅，3）衰减，4）散焦，5）由于有限的源-接收器孔径引起的差分辨率，以及6）由环状源子波引起的环状。为了部分地解决这些问题，最小二乘偏移（LSM），也称为线性化地震反演或偏移反褶积（MD），提出针对反射率分布线性地反演地震数据。如果偏移速度模型足够精确，则LSM可以减轻上述许多问题，并导致更高分辨率的偏移图像，有时具有两倍的空间分辨率。然而，LSM有两个问题：成本可能比标准偏移高一个数量级，并且对于5%或更大的速度误差，LSM图像的质量并不比标准图像好。我们现在展示如何通过降低LSM的成本和速度敏感性来最大限度地利用最小二乘偏移。
Standard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution due to limited source-receiver aperture, and 6) ringiness caused by a ringy source wavelet. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution. If the migration velocity model is sufficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image, sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for velocity errors of 5% or more. We now show how to get the most from leastsquares migration by reducing the cost and velocity sensitivity of LSM.