Movement of soil and rocks in an unstable slope under gravitational forces is an example of a complex system that is highly dynamic in space and time. A typical failure in such systems is a landslide. Fundamental studies of granular media failure combined with a complex network analysis of radar monitoring data show that distinct partitions emerge in the kinematic field in the early stages of the prefailure regime, and these patterns yield clues to the ultimate location of failure. In this study we address this partitioning of constituent units in complex systems by clustering the kinematic data, specifically, with a Gaussian mixture model. In addition, we assume that neighboring units should move together. As a result, spatial information is taken into account in our model so that spatial proximity is retained. Our case study of a rockslide from high resolution radar monitoring data shows that, by incorporating spatial information, our approach is more effective in revealing the dynamics of the system and detecting the location of a potential landslide, compared to the use of only the kinematics.

中文翻译：

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通过空间辅助高斯混合模型早期识别即将发生的滑坡位置

重力作用下不稳定斜坡中土壤和岩石的运动是一个复杂的系统的示例，该系统在时空上具有很高的动态性。这种系统的典型故障是滑坡。颗粒介质故障的基础研究与雷达监视数据的复杂网络分析相结合，显示出在故障前阶段的早期，运动场中出现了不同的分区，这些模式为故障的最终定位提供了线索。在这项研究中，我们通过对运动数据进行聚类（特别是使用高斯混合模型）来解决复杂系统中组成单元的这种划分。另外，我们假设相邻的单元应该一起移动。结果，在我们的模型中考虑了空间信息，因此保留了空间邻近性。