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Model structural uncertainty quantification and hydrologic parameter and prediction error analysis using airborne electromagnetic data

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Authors:
  • Minsley, B. J. ;
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    U.S. Geological SurveyCrustal Geophysics and Geochemistry Science CenterDenver
  • Christensen, Nikolaj Kruse ;
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    Department of Geoscience, Science and Technology, Aarhus University
  • Christensen, Steen ;
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    Orcid logo0000-0002-9251-2315
    Department of Geoscience, Science and Technology, Aarhus University
  • Ley-Cooper, Alan Yusen
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    Geoscience Australia
Abstract:
Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential indicator simulation, we produce many realizations of model structure that are consistent with observed datasets and prior knowledge. Given estimates of model structural uncertainty, we incorporate hydrologic observations to evaluate the errors in hydrologic parameter or prediction errors that occur when assumptions about model structure are imperfect.
Type:
Conference abstract
Language:
English
Main Research Area:
Science/technology
Review type:
Undetermined
Conference:
SAGEEP 2017
Submission year:
2017
Scientific Level:
Scientific
ID:
2354981519
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