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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Nikolaj K Christensen</dc:contributor>
  <dc:contributor>Steen Christensen</dc:contributor>
  <dc:contributor>Yusen Ley-Cooper</dc:contributor>
  <dc:creator>Burke J. Minsley</dc:creator>
  <dc:date>2021</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;A&lt;/span&gt;&lt;span&gt;irborne electromagnetic (AEM) data&lt;/span&gt;&lt;span&gt;are used&lt;/span&gt;&lt;span&gt;to &lt;/span&gt;&lt;span&gt;estimate large&lt;/span&gt;&lt;span&gt;-&lt;/span&gt;&lt;span&gt;scale model structural geometry, i.e. the &lt;/span&gt;&lt;span&gt;spatial distribution of different lit&lt;/span&gt;&lt;span&gt;hological units based on &lt;/span&gt;&lt;span&gt;assumed or estimated resistivity&lt;/span&gt;&lt;span&gt;-&lt;/span&gt;&lt;span&gt;lithology relationships, &lt;/span&gt;&lt;span&gt;and the uncertainty in those structures given imperfect &lt;/span&gt;&lt;span&gt;measurements. Geophysically derived estimates of model &lt;/span&gt;&lt;span&gt;structural uncertainty are then combined with hydrologic &lt;/span&gt;&lt;span&gt;obse&lt;/span&gt;&lt;span&gt;rvations to assess the impact of model structural &lt;/span&gt;&lt;span&gt;error on hydrologic calibration and prediction errors. &lt;/span&gt;&lt;span&gt;Using a synthetic numerical model, we describe a &lt;/span&gt;&lt;span&gt;sequential hydrogeophysical approach that: (1) uses &lt;/span&gt;&lt;span&gt;Bayesian Markov chain Monte Carlo (McMC) methods &lt;/span&gt;&lt;span&gt;to produce a robust estimate of uncertainty in electrical &lt;/span&gt;&lt;span&gt;resistivity parameter&lt;/span&gt;&lt;span&gt;s&lt;/span&gt;&lt;span&gt;, (2) combines geophysical &lt;/span&gt;&lt;span&gt;parameter &lt;/span&gt;&lt;span&gt;uncertainty &lt;/span&gt;&lt;span&gt;estimates &lt;/span&gt;&lt;span&gt;with &lt;/span&gt;&lt;span&gt;borehole &lt;/span&gt;&lt;span&gt;observations of lithology to produce probabilistic &lt;/span&gt;&lt;span&gt;estimates of model structural uncertainty over the e&lt;/span&gt;&lt;span&gt;ntire &lt;/span&gt;&lt;span&gt;AEM survey area using geostatistical sequential indicator &lt;/span&gt;&lt;span&gt;simulation algorithms, and (3) uses model structural &lt;/span&gt;&lt;span&gt;estimates along with hydrologic observations to quantify &lt;/span&gt;&lt;span&gt;both hydrologic parameter and prediction uncertainty &lt;/span&gt;&lt;span&gt;using a second McMC sampling &lt;/span&gt;&lt;span&gt;algorithm. Results of &lt;/span&gt;&lt;span&gt;simulations will be presented that illustrate the complete &lt;/span&gt;&lt;span&gt;workflow from geophysical parameter uncertainty &lt;/span&gt;&lt;span&gt;analysis to the impact of model structural uncertainty on &lt;/span&gt;&lt;span&gt;hydrologic parameter estimates. &lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:language>en</dc:language>
  <dc:publisher>Aarhus University</dc:publisher>
  <dc:title>Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data</dc:title>
  <dc:type>text</dc:type>
</oai_dc:dc>