scholarly journals Position Prediction of Marine Seismic Streamer Cables Using Various Kalman Filter Methods

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Jan Vidar Grindheim ◽  
Inge Revhaug ◽  
Egil Pedersen ◽  
Peder Solheim
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Computational Cost ◽  
Full Scale ◽  
Petroleum Exploration ◽  
Good Prediction ◽  
Filter Methods ◽  
Position Data ◽  
Path Prediction ◽  
Marine Seismic

Abstract Towed seismic streamer cables are extensively employed for offshore marine petroleum exploration. With the increasing need for accurate streamer steering due to rising number and length of streamers and decreasing intrastreamer separation, as well as new types of survey configurations, accurate modeling, positioning, and path prediction of the streamers are imperative. In the present study, a variety of models and methods have been implemented and utilized for data assimilation of full-scale seismic streamer position data for a marine seismic streamer, followed by path prediction ahead of time. The methods implemented are described, including various models used with the Kalman filter, extended Kalman filter, and ensemble Kalman filter, with comparison and evaluation of prediction results. One particular method, the Path-In-the-Water ensemble Kalman filter (PIW-EnKF), appears to be the most robust method with good prediction results compared to the other methods, as well as having low computational cost. As a case study with full-scale data, the PIW-EnKF is further employed for estimation and prediction of a complete streamer spread.

Comparison of Two Models for Prediction of Seismic Streamer State Using the Ensemble Kalman Filter

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Jan Vidar Grindheim ◽  
Inge Revhaug ◽  
Egil Pedersen ◽  
Peder Solheim
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Petroleum Exploration ◽  
Drift Angle ◽  
Pde Model ◽  
Survey Line ◽  
Position Data ◽  
Position Prediction ◽  
Increasing Demand ◽  
Marine Seismic

Towed marine seismic streamers are extensively utilized for petroleum exploration. With the increasing demand for efficiency, leading to longer and more densely spaced streamers, as well as four-dimensional (4D) surveys and more complicated survey configurations, the demand for optimal streamer steering has increased significantly. Accurate streamer state prediction is one important aspect of efficient streamer steering. In the present study, the ensemble Kalman filter (EnKF) has been used with two different models for data assimilation including parameter estimation followed by position prediction. The data used are processed position data for a seismic streamer at the very start of a survey line with particularly large cable movements due to currents. The first model is a partial differential equation (PDE) model reduced to two-dimensional (2D), solved using a finite difference method (FDM). The second model is based on a path-in-the-water (PIW) model and includes a drift angle. Prediction results using various settings are presented for both models. A variant of the PIW method gives the overall best results for the present data.

scholarly journals On the Choice of an Optimal Localization Radius in Ensemble Kalman Filter Methods

2014 ◽  
Vol 142 (6) ◽  
pp. 2165-2175 ◽  
Author(s):  
Paul Kirchgessner ◽  
Lars Nerger ◽  
Angelika Bunse-Gerstner
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Degrees Of Freedom ◽  
Localization Length ◽  
Ensemble Size ◽  
Localization Radius ◽  
Filter Methods ◽  
Local Observation ◽  
Optimal Localization ◽  
Analysis Quality

Abstract In data assimilation applications using ensemble Kalman filter methods, localization is necessary to make the method work with high-dimensional geophysical models. For ensemble square root Kalman filters, domain localization (DL) and observation localization (OL) are commonly used. Depending on the localization method, appropriate values have to be chosen for the localization parameters, such as the localization length and the weight function. Although frequently used, the properties of the localization techniques are not fully investigated. Thus, up to now an optimal choice for these parameters is a priori unknown and they are generally found by expensive numerical experiments. In this study, the relationship between the localization length and the ensemble size in DL and OL is studied using twin experiments with the Lorenz-96 model and a two-dimensional shallow-water model. For both models, it is found that the optimal localization length for DL and OL depends linearly on an effective local observation dimension that is given by the sum of the observation weights. In the experiments no influence of the model dynamics on the optimal localization length was observed. The effective observation dimension defines the degrees of freedom that are required for assimilating observations, while the ensemble size defines the available degrees of freedom. Setting the localization radius such that the effective local observation dimension equals the ensemble size yields an adaptive localization radius. Its performance is tested using a global ocean model. The experiments show that the analysis quality using the adaptive localization is similar to the analysis quality of an optimally tuned constant localization radius.

Assimilating Altimetry Data into a HYCOM Model of the Pacific: Ensemble Optimal Interpolation versus Ensemble Kalman Filter

2010 ◽  
Vol 27 (4) ◽  
pp. 753-765 ◽  
Author(s):  
Liying Wan ◽  
Laurent Bertino ◽  
Jiang Zhu
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Nonlinear Dynamical Systems ◽  
Computational Cost ◽  
Optimal Interpolation ◽  
Striking Difference ◽  
Primary Concern ◽  
Altimetry Data ◽  
Ensemble Optimal Interpolation ◽  
The Pacific

Abstract The ensemble Kalman filter (EnKF) has proven its efficiency in strongly nonlinear dynamical systems but is demanding in its computing power requirements, which are typically about the same as those of the four-dimensional variational data assimilation (4DVAR) systems presently used in several weather forecasting centers. A simplified version of EnKF, the so-called ensemble optimal interpolation (EnOI), requires only a small fraction of the computing cost of the EnKF, but makes the crude assumption of no dynamical evolution of the errors. How do both these two methods compare in realistic settings of a Pacific Ocean forecasting system where the computational cost is a primary concern? In this paper the two methods are used to assimilate real altimetry data via a Hybrid Coordinate Ocean Model of the Pacific. The results are validated against the independent Argo temperature and salinity profiles and show that the EnKF has the advantage in terms of both temperature and salinity and in all parts of the domain, although not with a very striking difference.

scholarly journals A Unification of Ensemble Square Root Kalman Filters

2012 ◽  
Vol 140 (7) ◽  
pp. 2335-2345 ◽  
Author(s):  
Lars Nerger ◽  
Tijana Janjić ◽  
Jens Schröter ◽  
Wolfgang Hiller
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Kalman Filters ◽  
Numerical Experiments ◽  
Computational Cost ◽  
Square Root ◽  
Symmetric Square ◽  
Ensemble Transform Kalman Filter ◽  
Filter Algorithms ◽  
Ensemble Transform

Abstract In recent years, several ensemble-based Kalman filter algorithms have been developed that have been classified as ensemble square root Kalman filters. Parallel to this development, the singular “evolutive” interpolated Kalman (SEIK) filter has been introduced and applied in several studies. Some publications note that the SEIK filter is an ensemble Kalman filter or even an ensemble square root Kalman filter. This study examines the relation of the SEIK filter to ensemble square root filters in detail. It shows that the SEIK filter is indeed an ensemble square root Kalman filter. Furthermore, a variant of the SEIK filter, the error subspace transform Kalman filter (ESTKF), is presented that results in identical ensemble transformations to those of the ensemble transform Kalman filter (ETKF), while having a slightly lower computational cost. Numerical experiments are conducted to compare the performance of three filters (SEIK, ETKF, and ESTKF) using deterministic and random ensemble transformations. The results show better performance for the ETKF and ESTKF methods over the SEIK filter as long as this filter is not applied with a symmetric square root. The findings unify the separate developments that have been performed for the SEIK filter and the other ensemble square root Kalman filters.

Application for Improved Awareness of Cable Geometry During Seismic Survey Operation

2019 ◽  
Author(s):  
Jan Vidar Grindheim ◽  
Ken E. Welker ◽  
Inge Revhaug
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Petroleum Exploration ◽  
Seismic Survey ◽  
Control Bird ◽  
Seismic Surveys ◽  
Sea Current ◽  
Towed Cable ◽  
And Control ◽  
Scale Data

Abstract Seismic surveys for petroleum exploration employ a number of towed cable streamers of lengths in the order of 3∼12 km. Accurate positioning is important both for navigation and control of the streamer spread and for seismic data processing. In the present study, a numerical streamer model based on a Finite Element Method is implemented both as an Extended Kalman filter and an Ensemble Kalman filter. The streamer model includes features such as tailbuoy, locally estimated sea current introduced at each node, control bird inputs, cable stretch and tension. The filter implementations are intended to blend the streamer model with observations such as acoustic range measurements and satellite navigation positioning. The implementations have been compared and validated employing full scale data for two line changes. The purpose is to investigate the filters’ performance during periods with poor or missing observations.

scholarly journals Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

2014 ◽  
Vol 14 (11) ◽  
pp. 2951-2973 ◽  
Author(s):  
M. C. Rochoux ◽  
S. Ricci ◽  
D. Lucor ◽  
B. Cuenot ◽  
A. Trouvé
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Surrogate Model ◽  
Polynomial Chaos ◽  
Computational Cost ◽  
Fire Spread ◽  
Data Driven ◽  
Fire Front ◽  
Wildfire Spread

Abstract. This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

scholarly journals Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

2014 ◽  
Vol 2 (5) ◽  
pp. 3289-3349 ◽  
Author(s):  
M. C. Rochoux ◽  
S. Ricci ◽  
D. Lucor ◽  
B. Cuenot ◽  
A. Trouvé
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Polynomial Chaos ◽  
Computational Cost ◽  
Fire Spread ◽  
Data Driven ◽  
Fire Front ◽  
Wildfire Spread

Abstract. This paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: a level-set-based fire propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the non-linearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially-uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based data assimilation algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically-generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of data assimilation strongly relate to the spatial and temporal variability of the errors in the ROS model parameters.

scholarly journals Assimilation of Tropical Cyclone Track and Wind Radius Data with an Ensemble Kalman Filter

2015 ◽  
Vol 30 (4) ◽  
pp. 1050-1063 ◽  
Author(s):  
Masaru Kunii
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclone ◽  
Ensemble Kalman Filter ◽  
Shape Parameters ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Sea Level Pressure ◽  
Position Information ◽  
Position Data ◽  
Minimum Sea Level Pressure

Abstract Improving tropical cyclone (TC) forecasts is one of the most important issues in meteorology, but TC intensity forecasting is a challenging task. Because the lack of observations near TCs usually results in degraded accuracy of the initial fields, utilizing TC advisory data in data assimilation typically has started with an ensemble Kalman filter (EnKF). In this study, TC minimum sea level pressure (MSLP) and position information were directly assimilated using the EnKF, and the impacts of these observations were investigated by comparing different assimilation strategies. Another experiment with TC wind radius data was carried out to examine the influence of TC shape parameters. Sensitivity experiments indicated that the direct assimilation of TC MSLP and position data yielded results that were superior to those based on conventional assimilation of TC MSLP as a standard surface pressure observation. Assimilation of TC radius data modified the outer circulation of TCs closer to observations. The impacts of these TC parameters were also evaluated by using the case of Typhoon Talas in 2011. The TC MSLP, position, and wind radius data led to improved TC track forecasts and therefore to improved precipitation forecasts. These results imply that initialization with these TC-related observations benefits TC forecasting, offering promise for the prevention and mitigation of natural disasters caused by TCs.

scholarly journals Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter

2019 ◽  
Vol 147 (9) ◽  
pp. 3283-3300
Author(s):  
Naila F. Raboudi ◽  
Boujemaa Ait-El-Fquih ◽  
Clint Dawson ◽  
Ibrahim Hoteit
Keyword(s):  
Kalman Filter ◽  
Storm Surge ◽  
Ensemble Kalman Filter ◽  
Computational Cost ◽  
Forecast Model ◽  
Bayesian Filtering ◽  
Background Error ◽  
One Step ◽  
Ensemble Transform Kalman Filter ◽  
Storm Surge Forecasting

Abstract This work combines two auxiliary techniques, namely the one-step-ahead (OSA) smoothing and the hybrid formulation, to boost the forecasting skills of a storm surge ensemble Kalman filter (EnKF) forecasting system. Bayesian filtering with OSA-smoothing enhances the robustness of the ensemble background statistics by exploiting the data twice: first to constrain the sampling of the forecast ensemble with the future observation, and then to update the resulting ensemble. This is expected to improve the behavior of EnKF-like schemes during the strongly nonlinear surges periods, but requires integrating the ensemble with the forecast model twice, which could be computationally demanding. The hybrid flow-dependent/static formulation of the EnKF background error covariance is then considered to enable the implementation of the filter with a small flow-dependent ensemble size, and thus less model runs. These two methods are combined within an ensemble transform Kalman filter (ETKF). The resulting hybrid ETKF with OSA smoothing is tested, based on twin experiments, using a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico and assimilating pseudo-observations of sea surface levels from a network of buoys. The results of our numerical experiments suggest that the proposed filtering system significantly enhances ADCIRC forecasting skills compared to the standard ETKF without increasing the computational cost.

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