During the three month long eruption of K\=ilauea volcano, Hawaii in 2018,
the pre-existing summit caldera collapsed in over 60 quasi-periodic failure
events. The last 40 of these events, which generated Mw >5 very long period
(VLP) earthquakes, had inter-event times between 0.8 - 2.2 days. These failure
events offer a unique dataset for testing methods for predicting earthquake
recurrence based on locally recorded GPS, tilt, and seismicity data. In this
work, we train a deep learning graph neural network (GNN) to predict the
time-to-failure of the caldera collapse events using only a fraction of the
data recorded at the start of each cycle. We find that the GNN generalizes to
unseen data and can predict the time-to-failure to within a few hours using
only 0.5 days of data, substantially improving upon a null model based only on
inter-event statistics. Predictions improve with increasing input data length,
and are most accurate when using high-SNR tilt-meter data. Applying the trained
GNN to synthetic data with different magma pressure decay times predicts
failure at a nearly constant stress threshold, revealing that the GNN is
sensing the underling physics of caldera collapse. These findings demonstrate
the predictability of caldera collapse sequences under well monitored
conditions, and highlight the potential of machine learning methods for
forecasting real world catastrophic events with limited training data.

该研究使用深度学习的图神经网络方法对 Kilauea 火山的火山口坍塌事件进行预测，发现这种方法能够在仅使用 0.5 天的数据时，准确地预测火山口坍塌的时间，并且使用高信噪比的倾斜仪数据时预测能力最强，这些结果展示了在监测良好的条件下对火山口坍塌序列的可预测性，并突出了机器学习方法在有限训练数据下预测真实世界灾难性事件的潜力。