During the three month long eruption of Kilauea 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.

翻译：2018年夏威夷基拉韦厄火山持续三个月的喷发期间，原有的山顶破火山口发生了超过60次准周期性崩塌事件。其中最后40次事件产生了矩震级>5的超长周期地震，事件间隔时间介于0.8至2.2天之间。这些破裂事件为基于局部GPS、倾斜仪及地震活动记录数据预测地震复发的方法提供了独特的数据集。本研究训练了一种深度学习图神经网络，仅利用每个周期初始阶段记录的部分数据，预测破火山口崩塌事件的发生时间。研究发现，该图神经网络能够泛化至未见数据，仅需0.5天的数据即可在数小时内精确预测崩塌时间，相较仅基于事件间隔统计的零模型有显著改进。预测精度随输入数据长度增加而提升，且在使用高信噪比倾斜仪数据时最为准确。将训练好的图神经网络应用于不同岩浆压力衰减时间的合成数据时，模型可在近似恒定应力阈值下预测破裂事件，表明该网络感知到了破火山口崩塌的底层物理机制。这些发现证明了在充分监测条件下破火山口崩塌序列的可预测性，并凸显了机器学习方法在有限训练数据条件下预测现实世界灾难性事件的潜力。