Department of Geoscience, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2023 Apr 15;23(8):4012. doi: 10.3390/s23084012.
Reverse-time migration (RTM) has the advantage that it can handle steep dipping structures and offer high-resolution images of the complex subsurface. Nevertheless, there are some limitations to the chosen initial model, aperture illumination and computation efficiency. RTM has a strong dependency on the initial velocity model. The RTM result image will perform poorly if the input background velocity model is inaccurate. One solution is to apply least-squares reverse-time migration (LSRTM), which updates the reflectivity and suppresses artifacts through iterations. However, the output resolution still depends heavily on the input and accuracy of the velocity model, even more than for standard RTM. For the aperture limitation, RTM with multiple reflections (RTMM) is instrumental in improving the illumination but will generate crosstalks because of the interference between different orders of multiples. We proposed a method based on a convolutional neural network (CNN) that behaves like a filter applying the inverse of the Hessian. This approach can learn patterns representing the relation between the reflectivity obtained through RTMM and the true reflectivity obtained from velocity models through a residual U-Net with an identity mapping. Once trained, this neural network can be used to enhance the quality of RTMM images. Numerical experiments show that RTMM-CNN can recover major structures and thin layers with higher resolution and improved accuracy compared with the RTM-CNN method. Additionally, the proposed method demonstrates a significant degree of generalizability across diverse geology models, encompassing complex thin layers, salt bodies, folds, and faults. Moreover, The computational efficiency of the method is demonstrated by its lower computational cost compared with LSRTM.
逆时偏移（RTM）具有能够处理陡倾构造并提供复杂地下高分辨率图像的优点。然而，所选择的初始模型、孔径照明和计算效率存在一些限制。RTM 对初始速度模型有很强的依赖性。如果输入的背景速度模型不准确，RTM 结果图像的性能将会很差。一种解决方案是应用最小二乘逆时偏移（LSRTM），通过迭代更新反射率并抑制伪影。然而，输出分辨率仍然严重依赖于输入和速度模型的准确性，甚至比标准 RTM 更依赖。对于孔径限制，多次反射的 RTM（RTMM）有助于提高照明效果，但由于不同阶多次反射之间的干扰，会产生串扰。我们提出了一种基于卷积神经网络（CNN）的方法，该方法的作用类似于应用 Hessian 逆的滤波器。这种方法可以通过具有恒等映射的残差 U-Net 学习表示通过 RTMM 获得的反射率与通过速度模型获得的真实反射率之间关系的模式。一旦经过训练，这个神经网络就可以用于增强 RTMM 图像的质量。数值实验表明，与 RTM-CNN 方法相比，RTMM-CNN 可以以更高的分辨率和改进的准确性恢复主要结构和薄层。此外，该方法在不同的地质模型中表现出很强的泛化能力，包括复杂的薄层、盐体、褶皱和断层。此外，与 LSRTM 相比，该方法的计算效率更高。
