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Abstract 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.
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This content will become publicly available on June 1, 2026
Graph neural networks for the localization of faults in a partially observed regional transmission system
Abstract Localization of faults in a large power system is one of the most important and difficult tasks of power systems monitoring. A fault, typically a shorted line, can be seen almost instantaneously by all measurement devices throughout the system, but determining its location in a geographically vast and topologically complex system is difficult. The task becomes even more difficult if measurements devices are placed only at some network nodes. We show that regression graph neural networks we construct, combined with a suitable statistical methodology, can solve this task very well. A chief advance of our methods is that we construct networks that produce localization without having being trained on data that contain fault localization information. We show that a synergy of statistics and deep learning can produce results that none of these approaches applied separately can achieve.
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- Award ID(s):
- 2123761
- PAR ID:
- 10633467
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Scandinavian Journal of Statistics
- Volume:
- 52
- Issue:
- 2
- ISSN:
- 0303-6898
- Page Range / eLocation ID:
- 572 to 594
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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