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UID:pretalx-flowpath-2025-JFCN8A@talks.staging.osgeo.org
DTSTART;TZID=CET:20250611T182000
DTEND;TZID=CET:20250611T183000
DESCRIPTION:In groundwater modeling\, uncertainty in parameters and predict
 ions can be reduced by history-matching the model against observations of 
 groundwater levels. When observations span different periods or come from 
 pumping tests\, complex calibration schemes may be required\, potentially 
 making optimization unfeasible. In such cases\, the modeler may choose to 
 lose information by reducing or simplifying the observation set in exchang
 e for a manageable process.\nThis study employs the Ensemble Space Inversi
 on (ENSI) methodology for history-matching a groundwater model of a comple
 x site against a large number of observations. ENSI estimates "super param
 eters" instead of native model parameters\, using an ensemble of random sa
 mples from prior parameter probability distributions. A key advantage of t
 his method is that it requires far fewer super parameters than model param
 eters\, reducing the number of model runs needed to calculate parameter se
 nsitivities.\nENSI was applied to a site characterized by heterogeneous py
 roclastic and volcanic deposits\, with three distinct aquifers. Various hy
 draulic tests were conducted to determine site-specific hydrogeological pa
 rameters\, assess vertical conductance\, derive pumping well characteristi
 cs\, and collect piezometric data. A six-layer numerical model was develop
 ed using MODFLOW-USG. Hydraulic parameters were calibrated via pilot point
 s\, with values interpolated through kriging.\nCompared to history-matchin
 g the same model using a standard GLM method applied to model parameters\,
  ENSI provided several advantages: fast convergence to low objective funct
 ion values\, significantly reduced execution time\, and natural parameter 
 field distributions.\nThough ENSI does not perform uncertainty estimation\
 , it offers a fast and effective way to obtain a single history-matched mo
 del\, which could serve as a starting point for ensemble-based uncertainty
  assessment.
DTSTAMP:20260427T221728Z
LOCATION:Room R3
SUMMARY:Efficient history-matching of a complex groundwater model using Ens
 emble Space Inversion - Mattia De Caro
URL:https://talks.staging.osgeo.org/flowpath-2025/talk/JFCN8A/
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