Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty

• Published in: Machine learning: science and technology Vol. 3; no. 4; pp. 45001 - 45013
• Main Authors: Izzatullah, Muhammad, Yildirim, Isa Eren, Waheed, Umair Bin, Alkhalifah, Tariq
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.12.2022
• Subjects: Approximation; Boundary conditions; Covariance matrix; deep learning; hypocenter localization; inversion; Laplace approximation; Localization; Mathematical analysis; microseismic; Microseisms; Neural networks; Partial differential equations; physics-informed neural networks (PINN); Propagation; Robustness (mathematics); Uncertainty; uncertainty quantification
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/ac94b3

Abstract Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibility of getting trapped in local minima, an alternate approach is needed that allows robust localization performance and holds the potential to make the elusive goal of real-time microseismic monitoring possible. Physics-informed neural networks (PINNs) have appeared on the scene as a flexible and versatile framework for solving partial differential equations (PDEs) along with the associated initial or boundary conditions. We develop HypoPINN —a PINN-based inversion framework for hypocenter localization and introduce an approximate Bayesian framework for estimating its predictive uncertainties. This work focuses on predicting the hypocenter locations using HypoPINN and investigates the propagation of uncertainties from the random realizations of HypoPINN’s weights and biases using the Laplace approximation. We train HypoPINN to obtain the optimized weights for predicting hypocenter location. Next, we approximate the covariance matrix at the optimized HypoPINN’s weights for posterior sampling with the Laplace approximation. The posterior samples represent various realizations of HypoPINN’s weights. Finally, we predict the locations of the hypocenter associated with those weights’ realizations to investigate the uncertainty propagation that comes from those realizations. We demonstrate the features of this methodology through several numerical examples, including using the Otway velocity model based on the Otway project in Australia.