Acoustic full‐waveform inversion of surface seismic data using the Gauss‐Newton method with active constraint balancing
Acoustic full‐waveform inversion of surface seismic data using the Gauss‐Newton method with active constraint balancing
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DOI：
10.1111/j.1365-2478.2012.01112.x
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发表时间：
2013-06
影响因子：
2.6
通讯作者：
Y. Joo;S. Seol;J. Byun
中科院分区：
文献类型：
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作者：
Y. Joo;S. Seol;J. Byun
我们提出了一种全波形反演算法，该算法使用具有主动约束平衡的高斯-牛顿反演方法，该方法使用频率域中地面地震数据的空间变化阻尼因子和源归一化波场方法。主动约束平衡技术通过使用参数分辨率矩阵和扩展函数分析自动确定控制高斯-牛顿反演中的稳定性和分辨率的阻尼因子的最佳分布。通过数值实验，我们提出了主动约束平衡方案提供了稳定的反演结果，而不会严重损失的分辨率相比，传统的高斯-牛顿方法。对于低灵敏度区域，使用主动约束平衡方法的重建图像更接近真实图像。此外，估计值比传统的高斯-牛顿法估计值更快地收敛到较小的RMS误差水平。我们还实现了归一化波场方法，以克服缺乏精确的知识的来源。源归一化波场方法有效地消除了源估计中潜在的反演误差，因为源谱在归一化过程中被消除了。我们的反演算法，使用源归一化方案，提供了良好的反演结果，即使数据是由两个略有不同的源子波。我们提出了Sirgue和Pratt提出的频率选择方案，该方案基于整个接收数据的平均幅度，为我们的反演选择合适的频率提供了有用的指导。我们的新的反演算法成功地重建速度模型在10-30次迭代，尽管它从一个均匀的或线性增加的速度模型。此外，为了测试我们的反演算法在一个更复杂的结构上的性能，我们将该算法应用于SEG/EAGE逆掩断层模型。当重建图像接近真实模型时，即使使用不充分的数据，也可以实现成功的反演，并且具有一致收敛的RMS误差。
We propose a full‐waveform inversion algorithm using the Gauss‐Newton inversion method with active constraint balancing that uses the spatially variant damping factor and source normalized wavefield approach for surface seismic data in the frequency domain. The active constraint balancing technique automatically determines the optimum distribution of damping factors that control the stability and resolution in Gauss‐Newton inversion by using a parameter resolution matrix and spread function analysis. Through numerical experiments, we present that the active constraint balancing scheme provides stable inversion results without a severe loss of resolution compared with the conventional Gauss‐Newton method. The reconstructed image using the active constraint balancing method more closely resembles the true image for the region with low sensitivity. Also, the estimated value converges faster to the smaller RMS error level than those estimated by the conventional Gauss‐Newton method. We also implement the normalized wavefield method to overcome the lack of precise knowledge on the source. The source normalized wavefield approach effectively removes the potential inversion errors from source estimation because the source spectrum is eliminated during the normalization procedure. Our inversion algorithm, using the source normalization scheme, provides excellent inversion results even though the data are generated by two slightly different source wavelets. We present that the frequency selection scheme proposed by Sirgue and Pratt, which is based on the average amplitude of the whole received data, provides a useful guideline for selecting the proper frequencies for our inversion. Our novel inversion algorithm successfully reconstructs the velocity model within 10–30 iterations despite its starting from a homogeneous or linearly increasing velocity model. In addition, for testing the performance of our inversion algorithm on a more complicated structure, we apply the algorithm to the SEG/EAGE overthrust model. Successful inversion is achieved as the reconstructed image approaches the true model with the consistently converging RMS error even though insufficient data are used.