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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 8; p. 4012
• Main Authors: Huang, Shang, Trad, Daniel
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15-04-2023; MDPI
• Subjects: Accuracy; Apertures; Computing time; convolutional neural network; Deep learning; Illumination; Image enhancement; Image quality; Neural networks; Propagation; Reflectance; reverse-time migration; surface-related multiples; Thin films; Velocity; Canada; Germany; reverse-time migration; convolutional neural network; surface-related multiples
• ISSN: 1424-8220; 1424-8220
• 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.