Sentence examples for multiple data assimilation from inspiring English sources

For history matching, we use the method known as ensemble smoother with multiple data assimilation.

This paper presents the results of an investigation on the performance of a variant of ES, namely, ensemble smoother with multiple data assimilation (ES-MDA), to history match production and seismic data of a real field.

In this case study, a particular data assimilation algorithm, the Ensemble Smoother with Multiple Data Assimilation (ESMDA) (Emerick and Reynolds, 2012), is used to derive posterior samples of uncertain model parameters and forecasts for a distributed hydrological model of Yanqi basin, China.

This work presents a study of an ensemble-based method, derived from the Kalman Filter (KF), the Ensemble Smoother with Multiple Data Assimilation (ES-MDA), in conjunction with a localization technique applied to a benchmark model with a known response, seeking to evaluate the final variability of the models and potential exclusion of better models in a HM problem.

In this paper, we propose a framework for conducting sequential data assimilation with multiple models and sources of data.

Here, we implement a linear dynamic model to (1) provide a scheme for data assimilation of multiple altimetric discharge along a river; (2) estimate daily discharge; (3) deal with data outages, and (4) smooth the estimated discharge.

However, for high dimensional systems, a new family of the factor graphs are developed in order to achieve an effective dimension reduction and to facilitate a synergetic application together with multiple graphs in addressing the Bayesian data assimilation.

The ARCHIPEL platform focuses on data assimilation and fusion from multiple sources.

In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms.

The ability of imaging algorithms to incorporate multiple types of data and use advanced inverse techniques borrowed from meteorological data assimilation to produce four-dimensional images of electron density is discussed therein.

We look forward to a future where operational data assimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations.