Identification of hydrological model parameter variation using ensemble Kalman filter
نویسندگان
چکیده
منابع مشابه
Real - Time Reservoir Model Updating Using Ensemble Kalman Filter
This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum ...
متن کاملIdentification of an Autonomous Underwater Vehicle Dynamic Using Extended Kalman Filter with ARMA Noise Model
In the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. Since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. In this study, hydrodynamic coefficients of an autonomous un...
متن کاملParameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter
A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles. The inverse problem is formulated as a sequential data integration problem. Gaussian Process Regression (GRP) is used to integrate the prior ...
متن کاملResampling the ensemble Kalman filter
Ensemble Kalman filters (EnKF) based on a small ensemble tend to provide collapse of the ensemble over time. It is shown that this collapse is caused by positive coupling of the ensemble members due to use of one common estimate of the Kalman gain for the update of all ensemble members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution i...
متن کاملMulti-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including s...
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: Hydrology and Earth System Sciences
سال: 2016
ISSN: 1607-7938
DOI: 10.5194/hess-20-4949-2016