Our recently developed tool, called Directed Affinity Segmentation (DAS), was originally designed for the data-driven discovery of coherent sets in fluidic systems. Here we interpret that it can also be used to indicate early warning signs of critical transitions in ice shelves as seen from remote sensing data. We apply a directed spectral clustering methodology, including an asymmetric affinity matrix and the associated directed graph Laplacian, to reprocess the ice velocity data and remote sensing satellite images of the Larsen C ice shelf. Our tool has enabled the simulated prediction of historical events from historical data and fault lines responsible for the critical transitions leading to the breakup of the Larsen C ice shelf crack, which resulted in the A-68 iceberg. Such benchmarking of methods, using data from the past to forecast events that are now also in the past, is sometimes called post-casting, analogous to forecasting into the future. Our method indicated the coming crisis months before the actual occurrence.

中文翻译：

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南极冰盖发生临界转变的预警信号–一种数据驱动的时空临界点工具

我们最近开发的称为定向亲和力分段（DAS）的工具最初是为数据驱动的流体系统中相干集的发现而设计的。在这里，我们解释说，它也可以用来指示从遥感数据中可以看到的冰架上关键转变的预警信号。我们应用定向光谱聚类方法，包括不对称亲和矩阵和相关的有向图拉普拉斯算子，以重新处理冰速数据和拉森C冰架的遥感卫星图像。我们的工具能够根据历史数据和断层线对历史事件进行模拟预测，这些断层线负责导致拉森C冰架裂缝破裂并导致A-68冰山破裂的关键转变。这样的基准测试方法，使用过去的数据来预测现在也是过去的事件，有时也称为后播，类似于对未来的预测。我们的方法在实际发生前几个月就预示着即将到来的危机。