Collaborative Research: Fusing Massive Disparate Data and Fast Surrogate Models for Probabilistic Quantification of Uncertain Hazards

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Mitigating the impact of natural hazards, such as volcanic eruptions, earthquakes, or infectious diseases, rests on our ability to accurately quantify hazard risks in advance of their occurrence. This project will tackle this challenge and develop a new computationally feasible framework to integrate disparate field observations and computer simulations. The new framework will deliver substantial upgrades in computational efficiency for natural hazard quantification. One testbed will be the 2018 eruption of the Kilauea Volcano in Hawaii, which injured 23 people and destroyed more than 700 dwellings. For this event, extensive field observations from disparate sources, such as radar satellites, global navigation satellite system receivers, borehole tiltmeters, and seismometers, as well as large-scale computer simulations, will be used to analyze methods for volcanic hazard quantification. The methods developed in the project will be implemented in open-source software available to a wide community of scientists and engineers. The project is complemented by training for both graduate and undergraduate students. The first major roadblock for precisely quantifying uncertain natural hazards is the computational scalability of computer simulations, as they often require the numerical solution of partial differential equations on massive spatio-temporal domains with multi-dimensional input. This challenge will be overcome by developing Gaussian process (GP) emulators as a computationally feasible surrogate model to approximate outcomes of computer experiments. This approach is appealing because it not only includes parallel predictions with linear computational order with respect to the number of coordinates, but it also leverages the correlation between coordinates to enable fast predictive sampling. The second computational challenge is in fusing disparate data from multiple sources to calibrate physical models. The project will address this challenge by quantifying uncertainty in data processing and estimating the discrepancy between the physical model and reality to allow for data integration. While this project focuses on applications in natural hazard quantification, the new GP emulator, computational tools for model calibration, and data integration methods will more generally extend the applicability of data science and machine learning algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.