Can Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?

We propose a binary classification model rooted in state-of-the-art deep learning techniques to predict whether or not complete-interface rupture is imminent along a numerical megathrust fault. The models are trained on labeled 2D space-time input features taken from the synthetic fault system. We contrast the performance of two neural networks trained on three types of data, to determine the relative predictive power of each. The neural networks are able to discriminate imminent complete rupture precursors from everything else, thus providing a relative size and time forecast. Vertical displacements along the fault demonstrate relatively good predictive power. The results confirm previous qualitative observations that precursory deformation scales with upcoming event size, consistent with the preslip model for earthquake nucleation. The methods we propose are adaptable and can be modified to use 3D data in the future. Plain Language Summary Unlike many other natural disasters, big earthquakes always take us by surprise. Despite many attempts, earthquake prediction has been historically unsuccessful. Some argue that this is due to a scarcity of data. Because many earthquakes take place along poorly instrumented fault zones, trends in precursory behavior are hard to find. This limitation is quickly changing, as technological advancements allow for the collection of massive quantities of data along earthquake-generating faults. Deep learning algorithms are well suited to find patterns in huge data sets. In this study, we use computer models to generate synthetic earthquake data sets and use deep learning to predict whether or not a big earthquake is imminent. We hope that this proof-of-concept model can guide future data collection and eventually be modified to forecast big earthquakes on real faults.

Automated summary

The study presents a binary classification model based on deep learning techniques to predict complete-interface rupture on a numerical megathrust fault. By comparing two neural networks trained on three types of data, it is demonstrated that the networks can differentiate imminent rupture precursors and provide relative size and time forecasts. The results confirm that precursory deformation scales with upcoming event size, and the proposed methods are adaptable for future use with 3D data.