Nothing Special   »   [go: up one dir, main page]

Schöniger et al., 2012 - Google Patents

Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography

Schöniger et al., 2012

View PDF @Full View
Document ID
11826003474082605668
Author
Schöniger A
Nowak W
Hendricks Franssen H
Publication year
Publication venue
Water Resources Research

External Links

Snippet

Ensemble Kalman filters (EnKFs) are a successful tool for estimating state variables in atmospheric and oceanic sciences. Recent research has prepared the EnKF for parameter estimation in groundwater applications. EnKFs are optimal in the sense of Bayesian …
Continue reading at agupubs.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V99/00—Subject matter not provided for in other groups of this subclass
    • G01V99/005—Geomodels or geomodelling, not related to particular measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/66—Subsurface modeling
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V11/00—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V9/00—Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00

Similar Documents

Publication Publication Date Title
Schöniger et al. Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography
Zhang et al. An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions
Zhang et al. An adaptive Gaussian process‐based method for efficient Bayesian experimental design in groundwater contaminant source identification problems
Day‐Lewis et al. Applying petrophysical models to radar travel time and electrical resistivity tomograms: Resolution‐dependent limitations
Nowak Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator
Irving et al. Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities
Cordua et al. Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information
Hansen et al. Accounting for imperfect forward modeling in geophysical inverse problems—exemplified for crosshole tomography
Huang et al. Robustness of joint interpretation of sequential pumping tests: Numerical and field experiments
Leube et al. Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design
Hermans et al. Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data
Zhang et al. Inverse modeling of hydrologic systems with adaptive multifidelity Markov chain Monte Carlo simulations
Li et al. Stochastic analysis of unsaturated flow with probabilistic collocation method
Rubin et al. A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields
Nowak et al. Geostatistical inference of hydraulic conductivity and dispersivities from hydraulic heads and tracer data
Puyguiraud et al. Stochastic dynamics of Lagrangian pore‐scale velocities in three‐dimensional porous media
Zhou et al. A pattern‐search‐based inverse method
Khodabakhshi et al. A Bayesian mixture‐modeling approach for flow‐conditioned multiple‐point statistical facies simulation from uncertain training images
Hakoun et al. Upscaling and prediction of Lagrangian velocity dynamics in heterogeneous porous media
Schulz et al. Importance of spatial structures in advancing hydrological sciences
Chen et al. Three‐dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data
Yin et al. Hydraulic tomography using temporal moments of drawdown recovery data: A laboratory sandbox study
Valstar et al. A representer‐based inverse method for groundwater flow and transport applications
Bianchi et al. An entrogram‐based approach to describe spatial heterogeneity with applications to solute transport in porous media
Tavakoli et al. Comparison of ensemble filtering algorithms and null‐space Monte Carlo for parameter estimation and uncertainty quantification using CO2 sequestration data