Delineating homogeneous domains of fractured rocks using topological manifolds and deep learning

Abstract

Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation. In this text, a technique that can integrate lithology, geotechnical and structural information is proposed to delineate homogeneous domains. This technique is then applied to a high and steep slope along a road. First, geological and geotechnical domains were described based on lithology, faults, and shear zones. Next, topological manifolds were used to eliminate the incompatibility between orientations and other parameters (i.e. trace length and roughness) so that the data concerning various properties of each discontinuity can be matched and characterized in the same Euclidean space. Thus, the influence of implicit combined effect in between parameter sequences on the homogeneous domains could be considered. Deep learning technique was employed to quantify abstract features of the characterization images of discontinuity properties, and to assess the similarity of rock mass structures. The results show that the technique can effectively distinguish structural variations and outperform conventional methods. It can handle multisource engineering geological information and multiple discontinuity parameters. This technique can also minimize the interference of human factors and delineate homogeneous domains based on orientations or multi-parameter with arbitrary distributions to satisfy different engineering requirements.

Keywords

• Homogeneous domain
• Geological domain
• Geotechnical domain
• Structural domain
• Topological manifold
• Deep learning