Article
Energy & Fuels
Farhana Akter, Syed Imtiaz, Sohrab Zendehboudi, Kamal Hossain
Summary: Ensemble Kalman filter (EnKF) is widely used in reservoir modelling for history matching, but faces challenges when dealing with mismatch between the reservoir and the model. By introducing modifications and conducting sensitivity analysis, improvements in history matching efficiency are possible in cases of high model mismatch and measurement uncertainty.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Chemistry, Analytical
Tipo Cui, Xiaohui Sun, Chenglin Wen
Summary: This paper proposes a new design method of sampling-driven Kalman filter to improve the performance of the filter for nonlinear systems. By integrating the advantages of UKF statistical sampling and EnKF random sampling, the shortcomings of both methods can be overcome. The new method obtains a large sample data ensemble through a new sampling mechanism, and optimizes the ensemble by selecting and assigning sample weights based on the centroid of the data ensemble, thus establishing a new Kalman filter.
Article
Meteorology & Atmospheric Sciences
Danian Liu, Yeqiang Shu, Dongxiao Wang, Weiqiang Wang, Tingting Zu, Wei Zhou
Summary: This study investigates the forecast of a strong eddy shedding event from the Kuroshio Loop Current (LC) during the winter 2016-2017, and the results indicate that assimilation of satellite sea surface height anomaly data in the subtropical countercurrent (STCC) region improves the forecast performance and assimilation in the Northern Equatorial Current (NEC) region also contributes to the formation of a strong Kuroshio LC and the occurrence of eddy shedding.
Article
Chemistry, Analytical
Sadaf Sarafan, Tai Le, Michael P. H. Lau, Afshan Hameed, Tadesse Ghirmai, Hung Cao
Summary: This study proposes a novel algorithm based on Ensemble Kalman filter for non-invasive fetal electrocardiogram extraction from a single-channel abdominal electrocardiogram signal. The proposed algorithm shows high accuracy and reliability, outperforming other algorithms according to the experimental data and the PhysioNet 2013 Challenge database validation.
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Environmental Sciences
Tarkeshwar Singh, Francois Counillon, Jerry Tjiputra, Yiguo Wang, Mohamad El Gharamti
Summary: This study demonstrates the ability of ensemble data assimilation methods to provide improved estimates of biogeochemical (BGC) model parameters and shows how BGC observations can effectively constrain errors in ocean physics. The method quickly converges and significantly reduces parameter errors.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Environmental Sciences
Mehrad Bayat, Hosein Alizadeh, Barat Mojaradi
Summary: This paper introduces the application of multivariate data assimilation (DA) to the SWAT model (DA-SWAT) and discusses the limitations of existing integrated approaches. A new approach is proposed that allows the perfect integration of SWAT with any desired DA algorithm. The results show that multivariate assimilation improves the accuracy of SCF (Streamflow) estimation and mitigates the equifinality problem.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
G. Piazzi, G. Thirel, C. Perrin, O. Delaigue
Summary: Skillful streamflow forecasts are crucial for water-related applications, with a growing emphasis on improving initial condition estimates through data assimilation. This study assesses the sensitivity of DA-based IC estimation to various uncertainties and model updates over 232 watersheds in France. The comparison of two ensemble-based techniques shows that accurate routing store estimates benefit the DA-based IC estimation, with the EnKF outperforming the PF in forecasting meteorological uncertainty.
WATER RESOURCES RESEARCH
(2021)
Article
Computer Science, Information Systems
Kevin R. Ford, Anton J. Haug
Summary: This study provides a concise derivation of the probability density function (PDF) for bearing in tracking a two-dimensional Cartesian state of a target using polar observations, and explores the limiting behavior of this distribution while parameterizing the target range.
Article
Mathematics, Applied
Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott
Summary: This study discusses the forecasting of chaotic dynamical systems using noisy partial measurements data, with a focus on combining machine learning with knowledge-based models to improve predictions. By assimilating synthetic data and training machine learning models with partial measurements, it shows potential to correct imperfections in knowledge-based models and improve forecasting accuracy.
Article
Engineering, Marine
Shaokun Deng, Zheqi Shen, Shengli Chen, Renxi Wang
Summary: The initial ensemble has an impact on the performance of ensemble-based assimilation techniques. The differences in the initial ensemble affect the convergence rate of assimilation, but all experiments eventually reach convergence. Sea surface height and sea surface salinity are more sensitive to the initial ensemble. The white-noise perturbation scheme has the largest effect, and the influence of different initial ensembles on sea surface height is concentrated in the region of the Antarctic Circumpolar Current.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Geochemistry & Geophysics
M. Kosary, E. Forootan, S. Farzaneh, M. Schumacher
Summary: This study presents a sequential calibration approach based on the Ensemble Kalman Filter to improve Total Electron Content (TEC) estimations in the ionosphere. The calibrated model, called 'C-EnKF-IRI', shows improved accuracy compared to existing models and can be used for real-time TEC estimations. The results demonstrate the importance of accurate ionospheric models in reducing the effects on Global Navigation Satellite Systems.
JOURNAL OF GEODESY
(2022)
Article
Computer Science, Theory & Methods
Jiangqi Wu, Linjie Wen, Peter L. Green, Jinglai Li, Simon Maskell
Summary: Many real-world problems require estimation of parameters of interest in a Bayesian framework from sequentially collected data. Conventional methods for sampling from posterior distributions do not efficiently address these problems as they do not consider the sequential structure of the data. Therefore, sequential methods like EnKF and SMCS are often used to update the posterior distribution and solve such problems.
STATISTICS AND COMPUTING
(2022)
Article
Multidisciplinary Sciences
Jann Paul Mattern, Christopher A. Edwards
Summary: To address the challenge of data assimilation in advanced marine ecosystem models, we propose a new implementation of the four-dimensional ensemble optimal interpolation (4dEnOI) technique. This technique does not require tangent linear or adjoint code, is easy to implement, and suitable for advanced ecosystem models. Test results on a simple marine ecosystem model show that the 4dEnOI method performs well in reducing model observation misfit.
Article
Meteorology & Atmospheric Sciences
Eviatar Bach, Michael Ghil
Summary: Data assimilation aims to optimally combine partial and noisy model forecasts and observations. Multi-model data assimilation generalizes the variational or Bayesian formulation of the Kalman filter and is proven to be the minimum variance linear unbiased estimator. In this study, a multi-model ensemble Kalman filter (MM-EnKF) based on this framework is formulated and implemented. The MM-EnKF can combine multiple model ensembles for both data assimilation and forecasting in a flow-dependent manner by providing adaptive model error estimation and matrix-valued weights for the separate models and observations. Numerical experiments using the Lorenz96 model show that the MM-EnKF results in significant error reductions compared to the best model and an unweighted multi-model ensemble in terms of probabilistic and deterministic metrics.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yuqing Chang, Rolf J. Lorentzen, Geir Naevdal, Tao Feng
COMPUTATIONAL GEOSCIENCES
(2020)
Article
Computer Science, Interdisciplinary Applications
Rolf J. Lorentzen, Tuhin Bhakta, Dario Grana, Xiaodong Luo, Randi Valestrand, Geir Naevdal
COMPUTATIONAL GEOSCIENCES
(2020)
Article
Geochemistry & Geophysics
Xingguo Huang, Kjersti Solberg Eikrem, Morten Jakobsen, Geir Naevdal
Article
Water Resources
Jan Magnusson, Geir Naevdal, Felix Matt, John F. Burkhart, Adam Winstral
HYDROLOGY RESEARCH
(2020)
Article
Physics, Mathematical
Xingguo Huang, Morten Jakobsen, Kjersti Solberg Eikrem, Geir Naevdal
COMMUNICATIONS IN COMPUTATIONAL PHYSICS
(2020)
Article
Geochemistry & Geophysics
Kjersti Solberg Eikrem, Geir Naevdal, Morten Jakobsen
Summary: This work uses the Lippmann-Schwinger equation to model seismic waves in strongly scattering acoustic media, improving efficiency by developing new preconditioners based on randomized matrix approximations and hierarchical matrices. Experimental results demonstrate the excellent performance of the method on two 2-D models.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Geochemistry & Geophysics
Kui Xiang, Kjersti Solberg Eikrem, Morten Jakobsen, Geir Naevdal
Summary: In this study, a convergent scattering series solution for the frequency-domain wave equation in acoustic media with variable density and velocity has been derived. Through the homotopy analysis method, an iterative solution for the vectorial Lippmann-Schwinger equation has been obtained, achieving convergence even in strongly scattering media. The computational cost of the developed algorithm scales as N2 and involves a convergence control operator selected using hierarchical matrices.
GEOPHYSICAL PROSPECTING
(2022)
Article
Energy & Fuels
Micheal B. Oguntola, Rolf J. Lorentzen
Summary: The paper introduces a new efficient, robust, and accurate optimal solution strategy for non-linear constrained optimization problems using the exterior penalty function method and the adaptive ensemble-based optimization approach. This strategy aims to address the issues faced with current constraint handling techniques in EnOpt method, showing faster convergence rate, accuracy, and robustness in solving practical optimization problems.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Energy & Fuels
Dean S. Oliver, Kristian Fossum, Tuhin Bhakta, Ivar Sando, Geir Naevdal, Rolf Johan Lorentzen
Summary: Reservoir simulation models require a large number of parameters to predict future reservoir behavior while being constrained by various factors to reduce uncertainty. The use of seismic surveys to observe changes in reservoir properties between wells offers potential in reducing prediction uncertainty, but faces challenges in practical applications.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Biophysics
Geir Naevdal, Einar K. Rofstad, Kjetil Soreide, Steinar Evje
Summary: Pancreatic cancer has a propensity for early metastasis, even for small early stage tumors. A computer model simulating tumor progression suggests high interstitial fluid pressure (IFP) as a possible driver for metastasis, shedding light on the clinical aggressiveness of pancreatic cancer.
JOURNAL OF BIOMECHANICS
(2022)
Article
Geochemistry & Geophysics
Kui Xiang, Morten Jakobsen, Kjersti Solberg Eikrem, Geir Naevdal
Summary: The distorted Born iterative method reduces a nonlinear inverse scattering problem to (ill-posed) linear inverse scattering problems that can be solved using a regularized least-squares formulation. It has been applied to electromagnetic and acoustic problems in three dimensions and to seismic problems for moderately large two-dimensional models.
GEOPHYSICAL PROSPECTING
(2023)
Article
Engineering, Mechanical
Steinar Evje, Hans Joakim Skadsem, Geir Naevdal
Summary: Conservation laws of the generic form c(t)+f(c)(x)=0 are important in engineering, but identifying unknown flux functions from observation data is challenging. This study explores a Bayesian method combined with iterative ensemble Kalman filtering to learn unknown nonlinear flux functions. Experimental results demonstrate the method's strong ability to identify unknown flux functions.
NONLINEAR DYNAMICS
(2023)
Article
Green & Sustainable Science & Technology
Kjersti Solberg Eikrem, Rolf Johan Lorentzen, Ricardo Faria, Andreas Storksen Stordal, Alexandre Godard
Summary: When planning wind farms, optimizing the layout is crucial for increasing production and reducing costs. This paper focuses on minimizing the levelized cost of energy (LCOE) for a floating wind farm using wind data in Porto Santo, Portugal. The ensemble based optimization (EnOpt) method is employed to handle the layout problem with multiple constraints, and the extended version called EPF-EnOpt outperforms other methods in reducing LCOE and computational cost. It is also found that EPF-EnOpt performs the best in maximizing annual energy production without considering costs.
Proceedings Paper
Engineering, Electrical & Electronic
Ulas Taskin, Kjersti Solberg Eikrem, Geir Naevdal, Morten Jakobsen, Dirk J. Verschuur, Koen W. A. van Dongen
PROCEEDINGS OF THE 2020 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS)
(2020)
