Anisotropic fast-marching algorithms are computationally efficient tools for generating realistic maps of karst conduit networks, constrained by both the spatial extent and the orientation of karstifiable geologic units. Existing models to generate conduit network maps are limited either by high computational requirements (for chemistry-based models) or by their inability to incorporate the effects of elevation and orientation gradients (for isotropic fast-marching models). The new anisotropic fast-marching approach described here provides a significant improvement, though it imitates rather than reproduces actual speleogenetic processes. It can rapidly generate a stochastic ensemble of plausible networks from basic geologic information, which can also be used as input to karst-appropriate flow models. This paper introduces an open-source, easy-to-use implementation through the Python package pyKasso, then describes its application to a well-mapped geologically complex long-term study site: the Gottesacker alpine karst system (Germany/Austria). Groundwater flow in this system is exceptionally well understood from speleological investigations and tracer tests. Conduit formation primarily occurs at the base of the karst aquifer, following plunging synclines. Although previous attempts to reproduce the conduit network at this site yielded implausible network maps, pyKasso quickly generated networks faithful to the known conduit system. However, the model was only able to generate these realistic networks when the inlet-outlet connections of the system were correctly assigned, highlighting the importance of pairing modeling efforts with field tracer tests. Therefore, a model ensemble method is also presented, to optimize field efforts by identifying the most informative tracer tests to perform.

各向异性快速行进算法是计算有效的工具，用于生成岩溶管道网络的真实地图，受空间范围和可岩溶地质单元方向的限制。生成管道网络图的现有模型要么受限于高计算要求（对于基于化学的模型），要么受限于它们无法结合高程和方向梯度的影响（对于各向同性快速行进模型）。这里描述的新的各向异性快速行进方法提供了显着的改进，尽管它模仿而不是再现实际的洞穴发生过程。它可以从基本地质信息中快速生成合理网络的随机集合，也可以用作适合岩溶流动模型的输入。本文介绍了一个开源的，通过 Python 包 pyKasso 进行易于使用的实现，然后描述了其在地质复杂的长期研究地点的应用：Gottesacker 高山岩溶系统（德国/奥地利）。从洞穴学调查和示踪测试中可以非常清楚地了解该系统中的地下水流。管道的形成主要发生在岩溶含水层的底部，跟随向斜倾斜。尽管以前在该站点复制管道网络的尝试产生了难以置信的网络图，但 pyKasso 迅速生成了忠实于已知管道系统的网络。然而，只有在正确分配系统的入口-出口连接时，该模型才能生成这些真实的网络，这突出了将建模工作与现场示踪测试配对的重要性。所以，