Lawrence Berkeley National Laboratory

The complex geometric features of subsurface fractures at different scales makes mesh generation challenging and/or expensive. In this paper, we make use of neural style transfer (NST), a machine learning technique, to generate mesh from rock fracture images. In this new approach, we use digital rock fractures at multiple scales that represent ‘content’ and define uniformly shaped and sized triangles to represent ‘style’. The 19-layer convolutional neural network (CNN) learns the content from the rock image, including lower-level features (such as edges and corners) and higher-level features (such as rock, fractures, or other mineral fillings), and learns the style from the triangular grids. By optimizing the cost function to achieve approximation to represent both the content and the style, numerical meshes can be generated and optimized. We utilize the NST to generate meshes for rough fractures with asperities formed in rock, a network of fractures embedded in rock, and a sand aggregate with multiple grains. Based on the examples, we show that this new NST technique can make mesh generation and optimization much more efficient by achieving a good balance between the density of the mesh and the presentation of the geometric features. Finally, we discuss future applications of this approach and perspectives of applying machine learning to bridge the gaps between numerical modeling and experiments.