ISSN 1004-4140
CN 11-3017/P
田坤, 张学涛, 李国磊. 添加正则化项的黏声逆时偏移成像方法研究[J]. CT理论与应用研究, 2017, 26(6): 669-677. DOI: 10.15953/j.1004-4140.2017.26.06.02
引用本文: 田坤, 张学涛, 李国磊. 添加正则化项的黏声逆时偏移成像方法研究[J]. CT理论与应用研究, 2017, 26(6): 669-677. DOI: 10.15953/j.1004-4140.2017.26.06.02
TIAN Kun, ZHANG Xue-tao, LI Guo-lei. Viscoacoustic Reverse Time Migration by Adding a Regularization Term[J]. CT Theory and Applications, 2017, 26(6): 669-677. DOI: 10.15953/j.1004-4140.2017.26.06.02
Citation: TIAN Kun, ZHANG Xue-tao, LI Guo-lei. Viscoacoustic Reverse Time Migration by Adding a Regularization Term[J]. CT Theory and Applications, 2017, 26(6): 669-677. DOI: 10.15953/j.1004-4140.2017.26.06.02

添加正则化项的黏声逆时偏移成像方法研究

Viscoacoustic Reverse Time Migration by Adding a Regularization Term

  • 摘要: 在实际地球介质中传播的地震波会产生衰减和频散现象,因此其更接近于黏弹性介质,在地震处理中补偿这些黏性影响是十分必要的。基于波动方程的叠前深度偏移中进行吸收衰减补偿更准确,也更有物理意义,直接求解双程波动方程的逆时偏移(RTM)能够成像大倾角复杂构造,具有诸多优势。然而当考虑吸收衰减补偿时通常会产生不稳定现象,大部分研究都是在逆时偏移的波场延拓中进行波数域的低通滤波来解决这个问题。本文采用广义标准线性固体的黏声波动方程进行吸收衰减补偿的Q--RTM方法,通过添加正则化项的方式来稳定延拓过程。添加正则化项本质上是低通滤波,滤波窗口是指数形式,在时空域有明确的形式,可以阻止发生高频不稳定。与直接滤波相比,正则化参数可以是空变的,因此比较适合剧烈变化的区域,我们还发现震源归一化互相关成像条件更适合Q--RTM方法。

     

    Abstract: The real geological medium is close to viscoelastic media in which seismic wave propagate with dispersion and attenuation. It is important to compensate the unwanted viscous effects in seismic processing. It is more accurate and physically more consistent to mitigate these effects in a wave-equation-based prestack depth migration. Historically, reverse time migration (RTM) based on directly solving the two-way wave equation has provided a superior way to image complex geologic regions. However, instability usually arises when considering compensation for absorption. Most researchers conduct high frequency filtering in wavenumber domain before or during wave field extrapolate in RTM to ensure stability. In this paper, we use viscoacoustic wave equation derived by Bai et al. (2013) to do Q-RTM and stabilize extrapolate by adding a regularization term. Compared with direct filtering, the regularization parameter can be space-varying. So this is suitable for severely variational regions. And we also find that source normalized cross-correlation imaging condition is more suitable in Q-RTM.

     

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