Data assimilation in large time-varying multidimensional fields
نویسندگان
چکیده
منابع مشابه
Data assimilation in large time-varying multidimensional fields
In the physical sciences, e.g., meteorology and oceanography, combining measurements with the dynamics of the underlying models is usually referred to as data assimilation. Data assimilation improves the reconstruction of the image fields of interest. Assimilating data with algorithms like the Kalman-Bucy filter (KBf) is challenging due to their computational cost which for two-dimensional (2-D...
متن کاملFast recursive reconstruction of large time varying multidimensional fields
We develop computationally fast and storage e cient implementations for the Kalman-Bucy lter (KBf) for data assimilation problems with large time varying multidimensional elds. We refer to them as the block KBf (bKBf) and the localized block KBf (lbKBf). For elds de ned on a 2D lattice of linear dimension I, the bKBf reduces the computational complexity of the KBf by O(I). The lbKBf saves furth...
متن کاملVisualizing highly multidimensional time varying Microseismic Events
Making decisions about improving an oil and gas reservoir model based upon microseismic data is a difficult challenge for reservoir engineers and analysts. These difficulties arise because the available data contains inaccuracies, has high-dimensionality and has a high degree of uncertainty. Currently these difficulties are intensified by the lack of computational tools to support interactive v...
متن کاملAccelerated Isosurface Extraction in Time-Varying Fields
ÐFor large time-varying data sets, memory and disk limitations can lower the performace of visualization applications. Algorithms and data structures must be explicitly designed to handle these data sets in order to achieve more interactive rates. The Temporal Branch-on-Need Octree (T-BON) extends the three-dimensional branch-on-need octree for time-varying isosurface extraction. This data stru...
متن کاملSummarizing Time-Varying Data
In generating textual summaries of data, the content determination problem is even more complicated when summarizing time-varying data, such as in weather or stockmarket report generation. As well as the maximum, minimum and mean, what is of interest is the behaviour of the variable over time; e.g. dramatic changes, trends and degree of variability in the data. For example, in the graph of temp...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 1999
ISSN: 1057-7149
DOI: 10.1109/83.799887