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Data Assimilation for Inversion

In: Model Calibration and Parameter Estimation

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ne-Zheng Sun & Alexander Sun, 2015. "Data Assimilation for Inversion," Springer Books, in: Model Calibration and Parameter Estimation, edition 127, chapter 9, pages 361-406, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4939-2323-6_9
    DOI: 10.1007/978-1-4939-2323-6_9
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