Adaptive multi-fidelity probabilistic collocation-based Kalman filter for subsurface flow data assimilation: numerical modeling and real-world experiment
Autor: | Lingzao Zeng, Qiang Zheng, Jun Man, Laosheng Wu |
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Rok vydání: | 2020 |
Předmět: |
Environmental Engineering
Polynomial chaos 010504 meteorology & atmospheric sciences Computer science Computation 0208 environmental biotechnology Probabilistic logic Computational intelligence 02 engineering and technology Kalman filter Collocation (remote sensing) 01 natural sciences 020801 environmental engineering Data assimilation Environmental Chemistry Ensemble Kalman filter Safety Risk Reliability and Quality Algorithm 0105 earth and related environmental sciences General Environmental Science Water Science and Technology |
Zdroj: | Stochastic Environmental Research and Risk Assessment. 34:1135-1146 |
ISSN: | 1436-3259 1436-3240 |
Popis: | The ensemble Kalman filter (EnKF) has received substantial attention in hydrologic data assimilation due to its ease of implementation. In EnKF, a large enough ensemble size is often required to ensure accuracy, which may result in considerable computational overhead, especially for large-scale problems. Motivated by recent developments in multi-fidelity simulation, we develop a novel data assimilation method that provides an alternative to EnKF, namely adaptive multi-fidelity probabilistic collocation-based Kalman filter (AMF-PCKF). The appealing feature is to approximate the system response with polynomial chaos expansion (PCE) using the adaptive multi-fidelity probabilistic collocation method, which improves the computational efficiency without sacrificing accuracy. This constitutes the forecast step of AMF-PCKF, while the analysis step is established by sequentially updating the PCE coefficients. As demonstrated by a synthetic numerical case of heat transport in unsaturated flow and a real-world two-phase flow experiment, AMF-PCKF can provide more accurate estimations than EnKF under the same amount of computation, even when the number of unknown parameters is as high as 100. |
Databáze: | OpenAIRE |
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