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Arxiv
3d
151
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A Deep Generative Model for the Simulation of Discrete Karst Networks
- The simulation of discrete karst networks is challenging due to complex physicochemical processes in geological and hydrogeological contexts.
- A novel approach using graph generative models to represent karst networks has been proposed.
- Karst networks are represented as graphs with nodes containing spatial information and edges indicating connections between nodes.
- The generative process involves utilizing graph recurrent neural networks (GraphRNN) to learn the topological distribution of karst networks.
- Denoising diffusion probabilistic models on graphs (G-DDPM) are used to learn node features like spatial coordinates.
- The approach aims to generate realistic karst networks that capture essential features of the original data.
- Real-world karst networks were used to test the approach by comparing generated subgraphs with actual subgraphs.
- Geometry and topology metrics were employed to evaluate the generated subgraphs.
- The methodology allows for stochastic simulation of discrete karst networks across various formations.
- It serves as a useful tool for studying physical processes such as flow and transport in karst environments.
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