资源环境科技发展态势分析平台

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.