5,315 research outputs found
4D Seismic History Matching Incorporating Unsupervised Learning
The work discussed and presented in this paper focuses on the history
matching of reservoirs by integrating 4D seismic data into the inversion
process using machine learning techniques. A new integrated scheme for the
reconstruction of petrophysical properties with a modified Ensemble Smoother
with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed.
The permeability field inside the reservoir is parametrised with an
unsupervised learning approach, namely K-means with Singular Value
Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit
(OMP) technique which is very typical for sparsity promoting regularisation
schemes. Moreover, seismic attributes, in particular, acoustic impedance, are
parametrised with the Discrete Cosine Transform (DCT). This novel combination
of techniques from machine learning, sparsity regularisation, seismic imaging
and history matching aims to address the ill-posedness of the inversion of
historical production data efficiently using ES-MDA. In the numerical
experiments provided, I demonstrate that these sparse representations of the
petrophysical properties and the seismic attributes enables to obtain better
production data matches to the true production data and to quantify the
propagating waterfront better compared to more traditional methods that do not
use comparable parametrisation techniques
A wildland fire model with data assimilation
A wildfire model is formulated based on balance equations for energy and
fuel, where the fuel loss due to combustion corresponds to the fuel reaction
rate. The resulting coupled partial differential equations have coefficients
that can be approximated from prior measurements of wildfires. An ensemble
Kalman filter technique with regularization is then used to assimilate
temperatures measured at selected points into running wildfire simulations. The
assimilation technique is able to modify the simulations to track the
measurements correctly even if the simulations were started with an erroneous
ignition location that is quite far away from the correct one.Comment: 35 pages, 12 figures; minor revision January 2008. Original version
available from http://www-math.cudenver.edu/ccm/report
Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter
The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and parameters. A new approach, the normal-score EnKF (NS-EnKF), has been recently proposed to handle complex aquifers with non-Gaussian distributed parameters. In this work, we aim at investigating the capacity of the NS-EnKF to identify patterns in the spatial distribution of the model parameters (hydraulic conductivities) by assimilating dynamic observations in the absence of direct measurements of the parameters themselves. In some situations, hydraulic conductivity measurements (hard data) may not be available, which requires the estimation of conductivities from indirect observations, such as piezometric heads. We show how the NS-EnKF is capable of retrieving the bimodal nature of a synthetic aquifer solely from piezometric head data. By comparison with a more standard implementation of the EnKF, the NS-EnKF gives better results with regard to histogram preservation, uncertainty assessment, and transport predictions. © 2011 International Association for Mathematical Geosciences.The authors gratefully acknowledge the financial support by the Spanish Ministry of Science and Innovation through project CGL2011-23295. The first author appreciates the financial aid from China Scholarship Council (CSC No. [2007]3020).Zhou, H.; Li, L.; Hendricks Franssen, H.; Gómez-Hernández, JJ. (2012). Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter. Mathematical Geosciences. 44(2):169-185. https://doi.org/10.1007/s11004-011-9372-3S169185442Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. 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Math Geol 25(3):329–355Li L, Zhou H, Gómez-Hernández JJ (2011a) A comparative study of three-dimensional hydraulic conductivity upscaling at the macrodispersion experiment (MADE) site, Columbus air force base, Mississippi (USA). J Hydrol 404(3–4):278–293Li L, Zhou H, Gómez-Hernández JJ (2011b) Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Adv Water Resour 34(4):478–489Moradkhani H, Sorooshian S, Gupta HV, Houser PR (2005) Dual state-parameter estimation of hydrological models using ensemble Kalman filter. Adv Water Resour 28:135–147Naevdal G, Johnsen L, Aanonsen S, Vefring E (Mar. 2005) Reservoir monitoring and continuous model updating using ensemble Kalman filter. SPE J 10(1):66–74Pardo-Igúzquiza E, Dowd PA (2003) CONNEC3D: a computer program for connectivity analysis of 3D random set models. 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Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
Development and evaluation of a Kalman-filter algorithm for terminal area navigation using sensors of moderate accuracy
Translational state estimation in terminal area operations, using a set of commonly available position, air data, and acceleration sensors, is described. Kalman filtering is applied to obtain maximum estimation accuracy from the sensors but feasibility in real-time computations requires a variety of approximations and devices aimed at minimizing the required computation time with only negligible loss of accuracy. Accuracy behavior throughout the terminal area, its relation to sensor accuracy, its effect on trajectory tracking errors and control activity in an automatic flight control system, and its adequacy in terms of existing criteria for various terminal area operations are examined. The principal investigative tool is a simulation of the system
Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution
Bayesian analysis is widely used in science and engineering for real-time
forecasting, decision making, and to help unravel the processes that explain
the observed data. These data are some deterministic and/or stochastic
transformations of the underlying parameters. A key task is then to summarize
the posterior distribution of these parameters. When models become too
difficult to analyze analytically, Monte Carlo methods can be used to
approximate the target distribution. Of these, Markov chain Monte Carlo (MCMC)
methods are particularly powerful. Such methods generate a random walk through
the parameter space and, under strict conditions of reversibility and
ergodicity, will successively visit solutions with frequency proportional to
the underlying target density. This requires a proposal distribution that
generates candidate solutions starting from an arbitrary initial state. The
speed of the sampled chains converging to the target distribution deteriorates
rapidly, however, with increasing parameter dimensionality. In this paper, we
introduce a new proposal distribution that enhances significantly the
efficiency of MCMC simulation for highly parameterized models. This proposal
distribution exploits the cross-covariance of model parameters, measurements
and model outputs, and generates candidate states much alike the analysis step
in the Kalman filter. We embed the Kalman-inspired proposal distribution in the
DREAM algorithm during burn-in, and present several numerical experiments with
complex, high-dimensional or multi-modal target distributions. Results
demonstrate that this new proposal distribution can greatly improve simulation
efficiency of MCMC. Specifically, we observe a speed-up on the order of 10-30
times for groundwater models with more than one-hundred parameters
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