Data Assimilation for Inversion

Ne-Zheng Sun () and Alexander Sun ()
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Ne-Zheng Sun: University of California at Los Angeles, Department of Civil and Environmental Engineering
Alexander Sun: University of Texas at Austin, Bureau of Economic Geology, Jackson School of Geosciences

Chapter 9 in Model Calibration and Parameter Estimation, 2015, pp 361-406 from Springer

Abstract: Abstract Many EWR applications are data centric and naturally call for the ability to fuse spatial and temporal information from multiple sources and in different formats and scales. With the rapid advance of in situ and remote sensing technologies and cyberinfrastructures, a major EWR research front in recent years has focused on the development of effective algorithms for integrating real-time data into prediction models. Oftentimes, prior knowledge and historic training data are limited in both quality and quantity, leading to uncertainties in calibrated models and estimated parameters. Such issues are relevant not only to distributed EWR models, but also to data-driven models, as we have seen in Chap. 8. A fundamental need in EWR is thus related to systematic and continuous extraction of useful information from new observations to provide updated estimates of model states and parameters. In this chapter, mathematical tools and methods that can be applied to automate such information fusion process will be introduced.

Keywords: Kalman Filter; Gaussian Mixture Model; Particle Filter; Extended Kalman Filter; Unscented Kalman Filter (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4939-2323-6_9

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