Artificial Karst system networks generation via random percolation models

Abstract

We compare the artificial Karst system networks generated via artificial noise with uniform random and a correlated noise (Perlin). Using a systematic process involving lattice generation, point selection, and a flood-fill algorithm to connect regions, we constructed networks and analyzed their structural properties. Our results highlight distinct patterns emerging from these noise types, showcasing their potential to model phenomena in real-world complex systems. This study contributes to complexity science by presenting a methodology for deriving networks from stochastic inputs.