The high-resolution waveform inversion for seismic velocities is gaining increasing interest as we start to deal with complex structures. Although full waveform inversion has been used for several years, obtaining high-resolution velocity models still presents many obstacles, such as the high computational cost and the limited band width of the data. Thus, we propose a deep learning-based algorithm to build high-resolution velocity models using low-resolution velocity models, migration images, and well-log velocities as inputs. The well information, specifically, helps enhance the resolution with ground truth information, especially around the well. These three inputs are fed to an improved neural network, a variant of U-Net, as three channels to predict the corresponding true velocity models, which serve as labels in the training. The incorporation of well velocities from several locations is crucial for improving the resolution of the output model. Numerical experiments on complex models demonstrate the robust performance of this network and the crucial role that well information plays, especially in generalizing the approach to models that differ from the trained ones and achieving superior performance compared to full waveform inversion.
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Electrical and Electronic Engineering