Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections

Abstract

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.

3D Reverse-Time Migration Imaging for Multiple Cross-Hole Research and Multiple Sensor Settings of Cross-Hole Seismic Exploration

“…However, it cannot perform high-precision imaging of complex structures, and it cannot fully utilize 3D wavefield information for imaging [28]. The reverse-time migration (RTM) method based on the wave equation can adapt to strong lateral variations in the velocity field and is considered one of the most accurate imaging methods [29][30][31]. In recent years, the 3D RTM method has been applied in the field with promising cross-hole results [32][33][34].…”

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines

“…Compared to conventional ultrasonic imaging methods, the RTM stands out as a seismic processing method founded on full-wave extrapolation. This approach encompasses information about mode switching and various wave reflections, enabling the generation of high-resolution images that effectively capture internal defects characterized by complex structures [47,48].…”