Sequential data assimilation with multiple nonlinear models and applications to subsurface flow

TL;DR

This paper introduces a new data assimilation framework that combines multiple nonlinear models with observational data to improve predictions in complex systems, demonstrated through subsurface flow applications.

Contribution

It develops a novel multi-model assimilation approach using extended, ensemble, and particle Kalman filters to address model uncertainty in nonlinear systems.

Findings

Enhanced forecast accuracy in subsurface flow simulations.

Effective integration of multiple models improves system estimation.

Framework applicable to various nonlinear system prediction tasks.

Abstract

Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicates one's prediction choices. Over the years, data assimilation techniques, such as the Kalman filter, have become essential tools for improved system estimation by incorporating both models forecast and measurement; but its potential to mitigate the impacts of aforementioned model-form uncertainty has yet to be developed. Based on an earlier study of Multi-model Kalman filter, we propose a novel framework to assimilate multiple models with observation data for nonlinear systems, using extended Kalman filter, ensemble Kalman filter and particle filter, respectively. Through numerical examples of subsurface flow, we demonstrate that the new assimilation framework provides an effective and improved forecast of system behaviour.