Bayesian neural network parameters provide insights into the earthquake rupture physics.

Sabber Ahamed

Keywords: Bayesian neural network, earthquake rupture, simulation, Explainable neural network

Abstract: I present a simple but informative approach to gain insight into the Bayesian neural network (BNN) trained parameters. I used 2000 dynamic rupture simulations to train a BNN model to predict if an earthquake can break through a simple 2D fault. In each simulation, fault geometry, stress conditions, and friction parameters vary. The trained BNN parameters show that the network learns the physics of earthquake rupture. Neurons with high positive weights contribute to the earthquake rupture and vice versa. The results show that the stress condition of the fault plays a critical role in determining its strength. The stress is also the top source of uncertainty, followed by the dynamic friction coefficient. When stress and friction drop of a fault have higher value and are combined with higher weighted neurons, the prediction score increases, thus fault likely to be ruptured. Fault's width and height have the least amount of uncertainty, which may not be correct in a real scenario. The study shows that the potentiality of BNN that provides data patterns about rupture physics to make an additional information source for scientists studying the earthquake rupture.

One-sentence Summary: A trained Bayesian Neural network parameter can provide information patterns to understand earthquake rupture physics.

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