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 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.

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 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.

Ensemble Kalman Filter for Out of Sequence Measurement Tracking

2012 ◽  
Vol 132 (10) ◽  
pp. 1617-1625
Author(s):  
Sirichai Pornsarayouth ◽  
Masaki Yamakita
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter

Data assimilation with the weighted ensemble Kalman filter

2010 ◽  
Author(s):  
Nicolas Papadakis ◽  
Etienne Mémin ◽  
Anne Cuzol ◽  
Nicolas Gengembre
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

scholarly journals Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter

2021 ◽  
Vol 11 (7) ◽  
pp. 2898
Author(s):  
Humberto C. Godinez ◽  
Esteban Rougier
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Failure Modes ◽  
Split Hopkinson Pressure Bar ◽  
Peak Stress ◽  
Discrete Element ◽  
Material Behavior ◽  
Model Parameters ◽  
Hopkinson Pressure Bar ◽  
Parameter Values

Simulation of fracture initiation, propagation, and arrest is a problem of interest for many applications in the scientific community. There are a number of numerical methods used for this purpose, and among the most widely accepted is the combined finite-discrete element method (FDEM). To model fracture with FDEM, material behavior is described by specifying a combination of elastic properties, strengths (in the normal and tangential directions), and energy dissipated in failure modes I and II, which are modeled by incorporating a parameterized softening curve defining a post-peak stress-displacement relationship unique to each material. In this work, we implement a data assimilation method to estimate key model parameter values with the objective of improving the calibration processes for FDEM fracture simulations. Specifically, we implement the ensemble Kalman filter assimilation method to the Hybrid Optimization Software Suite (HOSS), a FDEM-based code which was developed for the simulation of fracture and fragmentation behavior. We present a set of assimilation experiments to match the numerical results obtained for a Split Hopkinson Pressure Bar (SHPB) model with experimental observations for granite. We achieved this by calibrating a subset of model parameters. The results show a steady convergence of the assimilated parameter values towards observed time/stress curves from the SHPB observations. In particular, both tensile and shear strengths seem to be converging faster than the other parameters considered.

scholarly journals Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1520
Author(s):  
Zheng Jiang ◽  
Quanzhong Huang ◽  
Gendong Li ◽  
Guangyong Li
Keyword(s):  
Kalman Filter ◽  
Solute Transport ◽  
Ensemble Kalman Filter ◽  
Water Movement ◽  
Parameters Estimation ◽  
Saturated Soils ◽  
Hydrological Models ◽  
State Variables ◽  
Ensemble Size ◽  
Highly Nonlinear

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

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