Abstract Fault‐damage zones comprise multiscale fracture networks that may slip dynamically and interact with the main fault during earthquake rupture. Using 3D dynamic rupture simulations and scale‐dependent fracture energy, we examine dynamic interactions of more than 800 intersecting multiscale fractures surrounding a listric fault, emulating a major listric fault and its damage zone. We investigate 10 distinct orientations of maximum horizontal stress, probing the conditions necessary for sustained slip within the fracture network or activating the main fault. Additionally, we assess the feasibility of nucleating dynamic rupture earthquake cascades from a distant fracture and investigate the sensitivity of fracture network cascading rupture to the effective normal stress level. We model either pure cascades or main fault rupture with limited off‐fault slip. We find that cascading ruptures within the fracture network are dynamically feasible under certain conditions, including: (a) the fracture energy scales with fracture and fault size, (b) favorable relative pre‐stress of fractures within the ambient stress field, and (c) close proximity of fractures. We find that cascading rupture within the fracture network discourages rupture on the main fault. Our simulations suggest that fractures with favorable relative pre‐stress, embedded within a fault damage zone, may lead to cascading earthquake rupture that shadows main fault slip. We find that such off‐fault events may reach moment magnitudes up toMw ≈ 5.5, comparable to magnitudes that can be otherwise hosted by the main fault. Our findings offer insights into physical processes governing cascading earthquake dynamic rupture within multiscale fracture networks.
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Can Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?
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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- Award ID(s):
- 1723249
- PAR ID:
- 10447325
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 48
- Issue:
- 18
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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