Method of Anchored Distributions for Inverse Modelinghttp://mad.codeplex.com/project/feeds/rssThe Method of Anchored Distributions (MAD) is a Bayesian inversion technique for conditioning computational model parameters on relevant field observations. Updated Wiki: Documentationhttps://mad.codeplex.com/documentation?version=76<div class="wikidoc">
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=All%20Topics&referringTitle=What%20is%20MAD%3f">Click here for the MAD# wiki </a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=How%20do%20I%20install%20MAD%3f&referringTitle=All%20Topics" target="_blank">Requirements and installation</a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=Compiling%20MAD%20-%20Without%20Visual%20Studio">Compiling MAD#</a></strong></h3>
<h2><a href="https://mad.codeplex.com/wikipage?title=Contact"><strong>Contact us</strong></a></h2>
<p> </p>
<p><strong>MAD Papers:</strong></p>
<p>To view the published open abstracts, go to <a href="http://dx.doi.org">http://dx.doi.org</a> and enter the DOI.</p>
<p> </p>
<h2><strong>RECENTLY SUBMITTED ARTICLES<br>
</strong></h2>
<p>Osorio-Murrilo, C., H. Savoy, and Y. Rubin. “Managing forward models in an inverse modeling framework through a master driver approach.” 2016. Submitted to
<em>Computers and Geosciences. </em></p>
<div title="Page 2">
<div>
<div>
<p>Wang, C., H. Savoy, C. Osoirio-Murillo, X. Li, H. Zhu, and Y. Rubin. “Bayesian inverse modeling for probabilistic site characterization of shallow soil-tunneling system.” 2015. Submitted to
<em>Mathematical Geology</em>. </p>
</div>
</div>
</div>
<p> </p>
<h2><strong>PUBLISHED ARTICLES</strong></h2>
<p><strong>2015</strong></p>
<p><strong>Heße, F., H. Savoy, C. A. Osorio-Murillo, J. Sege, S. Attinger, and Y. Rubin (2015), Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion,
<em>Journal of Hydrology</em>, <em>531</em>, 73–87, doi:10.1016/j.jhydrol.2015.09.067.</strong></p>
<p>The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field,
defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity. In this study, we investigated the two features that are
often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands
to rea- son that the use of additional data could alleviate this problem. To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatilewith respect
to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests
or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process. Our findings suggest that both roughness and connectivity
have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the
solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</p>
<p> </p>
<p><strong>Over, M. W., U. Wollschlaeger, C. A. Osorio-Murillo, and Y. Rubin (2015), Bayesian inversion of Mualem-van Genuchten parameters in a multilayer soil profile: A data-driven assumption-free likelihood function,
<em>Water Resources Research</em>, <em>51</em>(2), 861–884, doi:10.1002/2013WR014956.</strong></p>
<p>This paper introduces a hierarchical simulation and modeling framework that allows for infer- ence and validation of the likelihood function in Bayesian inversion of vadose zone hydraulic properties. The likelihood function or its analogs (objective functions
and likelihood measures) are commonly assumed to be multivariate Gaussian in form; however, this assumption is not possible to verify without a hierarchical simulation and modeling framework. In this paper, we present the necessary statistical mechanisms for
uti- lizing the hierarchical framework. We apply the hierarchical framework to the inversion of the vadose zone hydraulic properties within a multilayer soil profile conditioned on moisture content observations collected in the uppermost four layers. The key
result of our work is that the goodness-of-fit validated likelihood func- tion form provides empirical justification for the assumption of multivariate Gaussian likelihood functions in past and future inversions at similar sites. As an alternative, the likelihood
function need not be assumed to follow a parametric statistical distribution and can be computed directly using nonparametric methods. The nonparametric methods are considerably more computationally demanding, and to demonstrate this approach, we present a
smaller dimension synthetic case study of evaporation from a soil column. The main drawback of our work is the increased computational expense of the inversion.</p>
<p> </p>
<p><strong>Osorio-Murillo, C. A., M. W. Over, H. Savoy, D. P. Ames, and Y. Rubin (2015), Software framework for inverse modeling and uncertainty characterization,
<em>Environmental Modelling & Software</em>, <em>66</em>, 98–109, doi:10.1016/j.envsoft.2015.01.002.</strong></p>
<p>Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing
SRFs, which is an imple- mentation of the Bayesian inverse modeling techniqueMethod of Anchored Distributions (MAD).MAD# allowsmodelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion
engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration,
external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters
characterizing a groundwater aquifer</p>
<p> </p>
<p><strong>2010-2014</strong></p>
<p>Over, M. W., X. Chen, Y. Yang, and Y. Rubin (2013), A strategy for improved computational efficiency of the method of anchored distributions, <em>Water Resour. Res.,</em> 49, 1-19, doi: 10.1002/wrcr.20182.</p>
<p>Y. Yang, M. Over, and Y. Rubin (2012) Strategic placement of localization devices (such as pilot points and anchors) in inverse modeling schemes,
<em>Water Resour. Res., </em>48, W08519, doi: 10.1029/2012WR011864.</p>
<p>X. Chen, H. Murakami, M. S. Hahn, G. E. Hammond, M. L. Rockhold, J. M. Zachara, and Y. Rubin (2012) Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data,
<em>Water Resour. Res.,</em> 48, W06501, doi: 10.1029/2011WR010675.</p>
<p>H. Murakami, X. Chen, M. S. Hahn, Y. Liu, M. L. Rockhold, V. R. Vermeul, J. M. Zachara, and Y. Rubin (2010) Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area,<em> Hydrol. Eart Syst. Sci.</em>, 14, doi: 10.5194/hess-14-1989-2010.</p>
<p>Rubin, Y., X. Chen, H. Murakami, and M. Hahn (2010), A Bayesian approach for inverse modeling, data assimilation,and conditional simulation of spatial random fields,
<em>Water Resour. Res.</em>, 46, W10523, doi:10.1029/2009WR008799.</p>
<p> </p>
<p><strong>MAD Abstracts:</strong></p>
<ul>
<li>Cucchi, K. et al. 2015 AGU Conference. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=1566872">A Bayesian inversion of hydrological and thermal parameters in the hyporheic zone</a>".
</li><li>Yu, A., et al. 2015 AGU Conference. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=1566873">Using Methods of Dimension Reduction to Expand Data Integration and Reduce Uncertainty in Hydrological and Geophysical Parameters</a>".
</li><li>Hesse, F., et al. 2014 AGU Conference. "Characterizing Roughness and Connectivity Properties of Aquifer Conductivity Using the Method of Anchored Distributions (MAD)".
</li><li>Osorio-Murrilo, C. A., et al. 2013 AGU Conference. "Parallelization of a spatial random field characterization process using the Method of Anchored Distributions and the HTCondor high throughput computing system".
</li><li>Rubin Y., et al. 2011 AGU Conference. "<a title="Demonstrating a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282175">Demonstrating
a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application</a>".
<ul>
<li>December 6, 2011 Afternoon Session </li></ul>
</li><li>Over M.W., et al. 2011 CUAHSI Conference. "<a title="Bringing Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282176">Bringing
Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)</a>".
</li><li>Ames D., et al. 2011 AGU Conference. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=283227">An Inverse Modeling Plugin for HydroDesktop using the Method of Anchored Distributions (MAD)</a>"
</li></ul>
<p><strong>MAD Presentation(s):</strong></p>
<ul>
<li>Slides from Over M. W., et al. 2011 CUAHSI Conference presentation. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282177">Bringing Inverse Modeling to the Scientific Community</a>"
</li><li>Slides from Ames D. P., et al. 2012 AGU Fall Meeting presentation. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=611282">A Modular GIS-based Software Architecture for Model Parameter Estimation using the Method of
Anchored Distributions (MAD)</a>" </li><li>http://www.youtube.com/watch?v=IqWM9Utr9zo </li></ul>
<p><strong>Archived Software Editions and Documentation:</strong></p>
<ul>
<li><a href="http://mad.codeplex.com/wikipage?title=Archived%20Material">Developmental Editions Material</a>
</li></ul>
<p>--------------------</p>
<p><a href="http://mad.codeplex.com/wikipage?title=Bundling">WRR Paper #2012WR011957R Supporting Material</a></p>
</div><div class="ClearBoth"></div>frystackaSun, 01 May 2016 20:34:02 GMTUpdated Wiki: Documentation 20160501083402PUpdated Wiki: Documentationhttps://mad.codeplex.com/documentation?version=75<div class="wikidoc">
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=All%20Topics&referringTitle=What%20is%20MAD%3f">Click here for the MAD# wiki </a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=How%20do%20I%20install%20MAD%3f&referringTitle=All%20Topics" target="_blank">Requirements and installation</a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=Compiling%20MAD%20-%20Without%20Visual%20Studio">Compiling MAD#</a></strong></h3>
<h2><a href="https://mad.codeplex.com/wikipage?title=Contact"><strong>Contact us</strong></a></h2>
<p> </p>
<p><strong>MAD Papers:</strong></p>
<p>To view the published open abstracts, go to <a href="http://dx.doi.org">http://dx.doi.org</a> and enter the DOI.</p>
<p> </p>
<h2><strong>RECENTLY SUBMITTED ARTICLES<br>
</strong></h2>
<p>Osorio-Murrilo, C., H. Savoy, and Y. Rubin. “Managing forward models in an inverse modeling framework through a master driver approach.” 2016. Submitted to
<em>Computers and Geosciences. </em></p>
<div title="Page 2">
<div>
<div>
<p>Wang, C., H. Savoy, C. Osoirio-Murillo, X. Li, H. Zhu, and Y. Rubin. “Bayesian inverse modeling for probabilistic site characterization of shallow soil-tunneling system.” 2015. Submitted to
<em>Mathematical Geology</em>. </p>
</div>
</div>
</div>
<p> </p>
<h2><strong>PUBLISHED ARTICLES</strong></h2>
<p><strong>2015</strong></p>
<p><strong>Heße, F., H. Savoy, C. A. Osorio-Murillo, J. Sege, S. Attinger, and Y. Rubin (2015), Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion,
<em>Journal of Hydrology</em>, <em>531</em>, 73–87, doi:10.1016/j.jhydrol.2015.09.067.</strong></p>
<p>The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field,
defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity. In this study, we investigated the two features that are
often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands
to rea- son that the use of additional data could alleviate this problem. To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatilewith respect
to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests
or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process. Our findings suggest that both roughness and connectivity
have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the
solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</p>
<p> </p>
<p><strong>Over, M. W., U. Wollschlaeger, C. A. Osorio-Murillo, and Y. Rubin (2015), Bayesian inversion of Mualem-van Genuchten parameters in a multilayer soil profile: A data-driven assumption-free likelihood function,
<em>Water Resources Research</em>, <em>51</em>(2), 861–884, doi:10.1002/2013WR014956.</strong></p>
<p>This paper introduces a hierarchical simulation and modeling framework that allows for infer- ence and validation of the likelihood function in Bayesian inversion of vadose zone hydraulic properties. The likelihood function or its analogs (objective functions
and likelihood measures) are commonly assumed to be multivariate Gaussian in form; however, this assumption is not possible to verify without a hierarchical simulation and modeling framework. In this paper, we present the necessary statistical mechanisms for
uti- lizing the hierarchical framework. We apply the hierarchical framework to the inversion of the vadose zone hydraulic properties within a multilayer soil profile conditioned on moisture content observations collected in the uppermost four layers. The key
result of our work is that the goodness-of-fit validated likelihood func- tion form provides empirical justification for the assumption of multivariate Gaussian likelihood functions in past and future inversions at similar sites. As an alternative, the likelihood
function need not be assumed to follow a parametric statistical distribution and can be computed directly using nonparametric methods. The nonparametric methods are considerably more computationally demanding, and to demonstrate this approach, we present a
smaller dimension synthetic case study of evaporation from a soil column. The main drawback of our work is the increased computational expense of the inversion.</p>
<p> </p>
<p><strong>Osorio-Murillo, C. A., M. W. Over, H. Savoy, D. P. Ames, and Y. Rubin (2015), Software framework for inverse modeling and uncertainty characterization,
<em>Environmental Modelling & Software</em>, <em>66</em>, 98–109, doi:10.1016/j.envsoft.2015.01.002.</strong></p>
<p>Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing
SRFs, which is an imple- mentation of the Bayesian inverse modeling techniqueMethod of Anchored Distributions (MAD).MAD# allowsmodelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion
engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration,
external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters
characterizing a groundwater aquifer</p>
<p> </p>
<p><strong>2010-2014</strong></p>
<p>Over, M. W., X. Chen, Y. Yang, and Y. Rubin (2013), A strategy for improved computational efficiency of the method of anchored distributions, <em>Water Resour. Res.,</em> 49, 1-19, doi: 10.1002/wrcr.20182.</p>
<p>Y. Yang, M. Over, and Y. Rubin (2012) Strategic placement of localization devices (such as pilot points and anchors) in inverse modeling schemes,
<em>Water Resour. Res., </em>48, W08519, doi: 10.1029/2012WR011864.</p>
<p>X. Chen, H. Murakami, M. S. Hahn, G. E. Hammond, M. L. Rockhold, J. M. Zachara, and Y. Rubin (2012) Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data,
<em>Water Resour. Res.,</em> 48, W06501, doi: 10.1029/2011WR010675.</p>
<p>H. Murakami, X. Chen, M. S. Hahn, Y. Liu, M. L. Rockhold, V. R. Vermeul, J. M. Zachara, and Y. Rubin (2010) Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area,<em> Hydrol. Eart Syst. Sci.</em>, 14, doi: 10.5194/hess-14-1989-2010.</p>
<p>Rubin, Y., X. Chen, H. Murakami, and M. Hahn (2010), A Bayesian approach for inverse modeling, data assimilation,and conditional simulation of spatial random fields,
<em>Water Resour. Res.</em>, 46, W10523, doi:10.1029/2009WR008799.</p>
<p> </p>
<p><strong>MAD Abstracts:</strong></p>
<ul>
<li>Cucchi, K. et al. 2015 AGU Conference. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=1566872">A Bayesian inversion of hydrological and thermal parameters in the hyporheic zone</a>".
</li><li>Yu, A., et al. 2015 AGU Conference. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=1566873">Using Methods of Dimension Reduction to Expand Data Integration and Reduce Uncertainty in Hydrological and Geophysical Parameters</a>".
</li><li>Rubin Y., et al. 2011 AGU Conference. "<a title="Demonstrating a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282175">Demonstrating
a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application</a>".
<ul>
<li>December 6, 2011 Afternoon Session </li></ul>
</li><li>Over M.W., et al. 2011 CUAHSI Conference. "<a title="Bringing Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282176">Bringing
Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)</a>".
</li><li>Ames D., et al. 2011 AGU Conference. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=283227">An Inverse Modeling Plugin for HydroDesktop using the Method of Anchored Distributions (MAD)</a>"
</li></ul>
<p><strong>MAD Presentation(s):</strong></p>
<ul>
<li>Slides from Over M. W., et al. 2011 CUAHSI Conference presentation. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282177">Bringing Inverse Modeling to the Scientific Community</a>"
</li><li>Slides from Ames D. P., et al. 2012 AGU Fall Meeting presentation. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=611282">A Modular GIS-based Software Architecture for Model Parameter Estimation using the Method of
Anchored Distributions (MAD)</a>" </li><li>http://www.youtube.com/watch?v=IqWM9Utr9zo </li></ul>
<p><strong>Archived Software Editions and Documentation:</strong></p>
<ul>
<li><a href="http://mad.codeplex.com/wikipage?title=Archived%20Material">Developmental Editions Material</a>
</li></ul>
<p>--------------------</p>
<p><a href="http://mad.codeplex.com/wikipage?title=Bundling">WRR Paper #2012WR011957R Supporting Material</a></p>
</div><div class="ClearBoth"></div>frystackaSun, 01 May 2016 20:27:59 GMTUpdated Wiki: Documentation 20160501082759PUpdated Wiki: Documentationhttps://mad.codeplex.com/documentation?version=74<div class="wikidoc">
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=All%20Topics&referringTitle=What%20is%20MAD%3f">Click here for the MAD# wiki </a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=How%20do%20I%20install%20MAD%3f&referringTitle=All%20Topics" target="_blank">Requirements and installation</a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=Compiling%20MAD%20-%20Without%20Visual%20Studio">Compiling MAD#</a></strong></h3>
<h2><a href="https://mad.codeplex.com/wikipage?title=Contact"><strong>Contact us</strong></a></h2>
<p> </p>
<p><strong>MAD Papers:</strong></p>
<p>To view the published open abstracts, go to <a href="http://dx.doi.org">http://dx.doi.org</a> and enter the DOI.</p>
<h2><strong>RECENTLY SUBMITTED ARTICLES<br>
</strong></h2>
<p>Osorio-Murrilo, C., H. Savoy, and Y. Rubin. “Managing forward models in an inverse modeling framework through a master driver approach.” 2016. Submitted to
<em>Computers and Geosciences. </em></p>
<div title="Page 2">
<div>
<div>
<p>Wang, C., H. Savoy, C. Osoirio-Murillo, X. Li, H. Zhu, and Y. Rubin. “Bayesian inverse modeling for probabilistic site characterization of shallow soil-tunneling system.” 2015. Submitted to
<em>Mathematical Geology</em>. </p>
</div>
</div>
</div>
<p> </p>
<h2><strong>PUBLISHED ARTICLES</strong></h2>
<p><strong>2015</strong></p>
<p><strong>Heße, F., H. Savoy, C. A. Osorio-Murillo, J. Sege, S. Attinger, and Y. Rubin (2015), Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion,
<em>Journal of Hydrology</em>, <em>531</em>, 73–87, doi:10.1016/j.jhydrol.2015.09.067.</strong></p>
<p>The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field,
defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity. In this study, we investigated the two features that are
often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands
to rea- son that the use of additional data could alleviate this problem. To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatilewith respect
to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests
or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process. Our findings suggest that both roughness and connectivity
have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the
solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</p>
<p> </p>
<p><strong>Over, M. W., U. Wollschlaeger, C. A. Osorio-Murillo, and Y. Rubin (2015), Bayesian inversion of Mualem-van Genuchten parameters in a multilayer soil profile: A data-driven assumption-free likelihood function,
<em>Water Resources Research</em>, <em>51</em>(2), 861–884, doi:10.1002/2013WR014956.</strong></p>
<p>This paper introduces a hierarchical simulation and modeling framework that allows for infer- ence and validation of the likelihood function in Bayesian inversion of vadose zone hydraulic properties. The likelihood function or its analogs (objective functions
and likelihood measures) are commonly assumed to be multivariate Gaussian in form; however, this assumption is not possible to verify without a hierarchical simulation and modeling framework. In this paper, we present the necessary statistical mechanisms for
uti- lizing the hierarchical framework. We apply the hierarchical framework to the inversion of the vadose zone hydraulic properties within a multilayer soil profile conditioned on moisture content observations collected in the uppermost four layers. The key
result of our work is that the goodness-of-fit validated likelihood func- tion form provides empirical justification for the assumption of multivariate Gaussian likelihood functions in past and future inversions at similar sites. As an alternative, the likelihood
function need not be assumed to follow a parametric statistical distribution and can be computed directly using nonparametric methods. The nonparametric methods are considerably more computationally demanding, and to demonstrate this approach, we present a
smaller dimension synthetic case study of evaporation from a soil column. The main drawback of our work is the increased computational expense of the inversion.</p>
<p> </p>
<p><strong>Osorio-Murillo, C. A., M. W. Over, H. Savoy, D. P. Ames, and Y. Rubin (2015), Software framework for inverse modeling and uncertainty characterization,
<em>Environmental Modelling & Software</em>, <em>66</em>, 98–109, doi:10.1016/j.envsoft.2015.01.002.</strong></p>
<p>Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing
SRFs, which is an imple- mentation of the Bayesian inverse modeling techniqueMethod of Anchored Distributions (MAD).MAD# allowsmodelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion
engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration,
external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters
characterizing a groundwater aquifer</p>
<p> </p>
<p><strong>2010-2014</strong></p>
<p>Over, M. W., X. Chen, Y. Yang, and Y. Rubin (2013), A strategy for improved computational efficiency of the method of anchored distributions, <em>Water Resour. Res.,</em> 49, 1-19, doi: 10.1002/wrcr.20182.</p>
<p>Y. Yang, M. Over, and Y. Rubin (2012) Strategic placement of localization devices (such as pilot points and anchors) in inverse modeling schemes,
<em>Water Resour. Res., </em>48, W08519, doi: 10.1029/2012WR011864.</p>
<p>X. Chen, H. Murakami, M. S. Hahn, G. E. Hammond, M. L. Rockhold, J. M. Zachara, and Y. Rubin (2012) Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data,
<em>Water Resour. Res.,</em> 48, W06501, doi: 10.1029/2011WR010675.</p>
<p>H. Murakami, X. Chen, M. S. Hahn, Y. Liu, M. L. Rockhold, V. R. Vermeul, J. M. Zachara, and Y. Rubin (2010) Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area,<em> Hydrol. Eart Syst. Sci.</em>, 14, doi: 10.5194/hess-14-1989-2010.</p>
<p>Rubin, Y., X. Chen, H. Murakami, and M. Hahn (2010), A Bayesian approach for inverse modeling, data assimilation,and conditional simulation of spatial random fields,
<em>Water Resour. Res.</em>, 46, W10523, doi:10.1029/2009WR008799.</p>
<p> </p>
<p><strong>MAD Abstracts:</strong></p>
<ul>
<li>Rubin Y., et al. 2011 AGU Conference. "<a title="Demonstrating a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282175">Demonstrating
a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application</a>".
<ul>
<li>December 6, 2011 Afternoon Session </li></ul>
</li><li>Over M.W., et al. 2011 CUAHSI Conference. "<a title="Bringing Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282176">Bringing
Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)</a>".
</li><li>Ames D., et al. 2011 AGU Conference. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=283227">An Inverse Modeling Plugin for HydroDesktop using the Method of Anchored Distributions (MAD)</a>"
</li></ul>
<p><strong>MAD Presentation(s):</strong></p>
<ul>
<li>Slides from Over M. W., et al. 2011 CUAHSI Conference presentation. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282177">Bringing Inverse Modeling to the Scientific Community</a>"
</li><li>Slides from Ames D. P., et al. 2012 AGU Fall Meeting presentation. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=611282">A Modular GIS-based Software Architecture for Model Parameter Estimation using the Method of
Anchored Distributions (MAD)</a>" </li><li>http://www.youtube.com/watch?v=IqWM9Utr9zo </li></ul>
<p><strong>Archived Software Editions and Documentation:</strong></p>
<ul>
<li><a href="http://mad.codeplex.com/wikipage?title=Archived%20Material">Developmental Editions Material</a>
</li></ul>
<p>--------------------</p>
<p><a href="http://mad.codeplex.com/wikipage?title=Bundling">WRR Paper #2012WR011957R Supporting Material</a></p>
</div><div class="ClearBoth"></div>frystackaSun, 01 May 2016 20:16:26 GMTUpdated Wiki: Documentation 20160501081626PUpdated Wiki: Documentationhttps://mad.codeplex.com/documentation?version=73<div class="wikidoc">
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=All%20Topics&referringTitle=What%20is%20MAD%3f">Click here for the MAD# wiki </a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=How%20do%20I%20install%20MAD%3f&referringTitle=All%20Topics" target="_blank">Requirements and installation</a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=Compiling%20MAD%20-%20Without%20Visual%20Studio">Compiling MAD#</a></strong></h3>
<h2><a href="https://mad.codeplex.com/wikipage?title=Contact"><strong>Contact us</strong></a></h2>
<p> </p>
<p><strong>MAD Papers:</strong></p>
<p>To view the published open abstracts, go to <a href="http://dx.doi.org">http://dx.doi.org</a> and enter the DOI.</p>
<p><strong>RECENTLY SUBMITTED ARTICLES<br>
</strong></p>
<p>Osorio-Murrilo, C., H. Savoy, and Y. Rubin. “Managing forward models in an inverse modeling framework through a master driver approach.” 2016. Submitted to
<em>Computers and Geosciences. </em></p>
<div title="Page 2">
<div>
<div>
<p>Wang, C., H. Savoy, C. Osoirio-Murillo, X. Li, H. Zhu, and Y. Rubin. “Bayesian inverse modeling for probabilistic site characterization of shallow soil-tunneling system.” 2015. Submitted to
<em>Mathematical Geology</em>. </p>
</div>
</div>
</div>
<p> </p>
<p><strong>PUBLISHED ARTICLES</strong></p>
<p><strong>2015</strong></p>
<p>Heße, F., H. Savoy, C. A. Osorio-Murillo, J. Sege, S. Attinger, and Y. Rubin (2015), Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion,
<em>J. Hydrol.</em>, <em>531</em>, 73–87, doi:10.1016/j.jhydrol.2015.09.067.</p>
<p>The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field,
defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity. In this study, we investigated the two features that are
often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands
to rea- son that the use of additional data could alleviate this problem. To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatilewith respect
to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests
or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process. Our findings suggest that both roughness and connectivity
have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the
solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</p>
<p> </p>
<p>Over, M. W., U. Wollschlaeger, C. A. Osorio-Murillo, and Y. Rubin (2015), Bayesian inversion of Mualem-van Genuchten parameters in a multilayer soil profile: A data-driven assumption-free likelihood function,
<em>Water Resources Research</em>, <em>51</em>(2), 861–884, doi:10.1002/2013WR014956.</p>
<p>This paper introduces a hierarchical simulation and modeling framework that allows for infer- ence and validation of the likelihood function in Bayesian inversion of vadose zone hydraulic properties. The likelihood function or its analogs (objective functions
and likelihood measures) are commonly assumed to be multivariate Gaussian in form; however, this assumption is not possible to verify without a hierarchical simulation and modeling framework. In this paper, we present the necessary statistical mechanisms for
uti- lizing the hierarchical framework. We apply the hierarchical framework to the inversion of the vadose zone hydraulic properties within a multilayer soil profile conditioned on moisture content observations collected in the uppermost four layers. The key
result of our work is that the goodness-of-fit validated likelihood func- tion form provides empirical justification for the assumption of multivariate Gaussian likelihood functions in past and future inversions at similar sites. As an alternative, the likelihood
function need not be assumed to follow a parametric statistical distribution and can be computed directly using nonparametric methods. The nonparametric methods are considerably more computationally demanding, and to demonstrate this approach, we present a
smaller dimension synthetic case study of evaporation from a soil column. The main drawback of our work is the increased computational expense of the inversion.</p>
<p> </p>
<p>Osorio-Murillo, C. A., M. W. Over, H. Savoy, D. P. Ames, and Y. Rubin (2015), Software framework for inverse modeling and uncertainty characterization,
<em>Environ. Model. Softw.</em>, <em>66</em>, 98–109, doi:10.1016/j.envsoft.2015.01.002.</p>
<p>Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing
SRFs, which is an imple- mentation of the Bayesian inverse modeling techniqueMethod of Anchored Distributions (MAD).MAD# allowsmodelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion
engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration,
external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters
characterizing a groundwater aquifer</p>
<p> </p>
<p><strong>2010-2014</strong></p>
<p>Over, M. W., X. Chen, Y. Yang, and Y. Rubin (2013), A strategy for improved computational efficiency of the method of anchored distributions, <em>Water Resour. Res.,</em> 49, 1-19, doi: 10.1002/wrcr.20182.</p>
<p>Y. Yang, M. Over, and Y. Rubin (2012) Strategic placement of localization devices (such as pilot points and anchors) in inverse modeling schemes,
<em>Water Resour. Res., </em>48, W08519, doi: 10.1029/2012WR011864.</p>
<p>X. Chen, H. Murakami, M. S. Hahn, G. E. Hammond, M. L. Rockhold, J. M. Zachara, and Y. Rubin (2012) Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data,
<em>Water Resour. Res.,</em> 48, W06501, doi: 10.1029/2011WR010675.</p>
<p>H. Murakami, X. Chen, M. S. Hahn, Y. Liu, M. L. Rockhold, V. R. Vermeul, J. M. Zachara, and Y. Rubin (2010) Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area,<em> Hydrol. Eart Syst. Sci.</em>, 14, doi: 10.5194/hess-14-1989-2010.</p>
<p>Rubin, Y., X. Chen, H. Murakami, and M. Hahn (2010), A Bayesian approach for inverse modeling, data assimilation,and conditional simulation of spatial random fields,
<em>Water Resour. Res.</em>, 46, W10523, doi:10.1029/2009WR008799.</p>
<p> </p>
<p><strong>MAD Abstracts:</strong></p>
<ul>
<li>Rubin Y., et al. 2011 AGU Conference. "<a title="Demonstrating a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282175">Demonstrating
a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application</a>".
<ul>
<li>December 6, 2011 Afternoon Session </li></ul>
</li><li>Over M.W., et al. 2011 CUAHSI Conference. "<a title="Bringing Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282176">Bringing
Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)</a>".
</li><li>Ames D., et al. 2011 AGU Conference. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=283227">An Inverse Modeling Plugin for HydroDesktop using the Method of Anchored Distributions (MAD)</a>"
</li></ul>
<p><strong>MAD Presentation(s):</strong></p>
<ul>
<li>Slides from Over M. W., et al. 2011 CUAHSI Conference presentation. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282177">Bringing Inverse Modeling to the Scientific Community</a>"
</li><li>Slides from Ames D. P., et al. 2012 AGU Fall Meeting presentation. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=611282">A Modular GIS-based Software Architecture for Model Parameter Estimation using the Method of
Anchored Distributions (MAD)</a>" </li><li>http://www.youtube.com/watch?v=IqWM9Utr9zo </li></ul>
<p><strong>Archived Software Editions and Documentation:</strong></p>
<ul>
<li><a href="http://mad.codeplex.com/wikipage?title=Archived%20Material">Developmental Editions Material</a>
</li></ul>
<p>--------------------</p>
<p><a href="http://mad.codeplex.com/wikipage?title=Bundling">WRR Paper #2012WR011957R Supporting Material</a></p>
</div><div class="ClearBoth"></div>frystackaSun, 01 May 2016 20:14:01 GMTUpdated Wiki: Documentation 20160501081401PUpdated Wiki: Homehttps://mad.codeplex.com/wikipage?version=23<div class="wikidoc">
<h2 style="text-align:center"><strong>The Method of Anchored Distributions (MAD): Principles and Implementation as a Community Resource</strong></h2>
<p>For a listing of MAD-related publications, see the <a href="http://mad.codeplex.com/documentation">
Documentation</a> page. </p>
<p>In 2010, we proposed to develop a community-based, open-source computational platform for Bayesian characterization of uncertain parameter fields. It was motivated by the need to provide easily-accessible tools for applications and a platform for broad-based,
long-term development, built to meet the challenges brought upon by the ever increasing complexity of modern computational tools, the diversity of data types and the breadth and depth of the subject matters needed for applications. The initiation of such an
effort was intended to emulate the success experienced by other communities with a long tradition of community-based development efforts, and our hope was that it could lead to a transformative change in hydrology. The absence of such an approach had been
hurting the science discovery process. </p>
<p>Our project was to construct a modular, open-source computational platform for application and testing of the general statistical framework for parameter field characterization. <span style="font-size:10pt">Our approach was built around
two concepts that make it applicable to a wide range of data types and computational tools. The first concept is a classification of data into two broad categories. The second concept is a strategy for localization of data using anchors (hence Method of Anchored
Distributions, or MAD) that captures the relevant information contained in a local or non-local process, respectively, and presents it in terms relevant for the parameter field(s). </span></p>
<p>We have constructed a modular computational platform, with 2 distinct modules, which deliver the capabilities necessary to support the aforementioned concepts: namely, a pre-processing block and a post-processing block. The modules comprise the MAD kernel.
The design of the user-interface allows users and developers to easily link the kernel with their models via a driver. Many of the decisions over assumptions to be adopted or computational tools to be employed are at the user’s discretion and are not
hard-wired into the kernel. The kernel is built using open-source architecture.</p>
<p><span style="font-size:10pt">Eventually, two versions of MAD will be available: a super-computing version (for large-scale applications - in development) and a desktop application (for educational purposes and smaller-scale applications). Also, the kernel
will be eventually interoperable with a wider array of community-based resources (such as CUAHSI’s hydrologic information systems) and is already interoperable with some libraries (such as R). With this framework for generic application of the
concepts and principles of MAD in place, further types of data can be introduced, simplifying assumptions can be added or removed at the user’s discretion, and powerful numerical models and analytical tools, current or under development, can be integrated
much more easily in future analyses under long-term community-based development to follow. </span></p>
<p> </p>
</div><div class="ClearBoth"></div>frystackaSun, 01 May 2016 20:10:10 GMTUpdated Wiki: Home 20160501081010PUpdated Wiki: Homehttps://mad.codeplex.com/wikipage?version=22<div class="wikidoc">
<h2 style="text-align:center"><strong>The Method of Anchored Distributions (MAD): Principles and Implementation as a Community Resource</strong></h2>
<p>In 2010, we proposed to develop a community-based, open-source computational platform for Bayesian characterization of uncertain parameter fields. It was motivated by the need to provide easily-accessible tools for applications and a platform for broad-based,
long-term development, built to meet the challenges brought upon by the ever increasing complexity of modern computational tools, the diversity of data types and the breadth and depth of the subject matters needed for applications. The initiation of such an
effort was intended to emulate the success experienced by other communities with a long tradition of community-based development efforts, and our hope was that it could lead to a transformative change in hydrology. The absence of such an approach had been
hurting the science discovery process. </p>
<p>Our project was to construct a modular, open-source computational platform for application and testing of the general statistical framework for parameter field characterization. <span style="font-size:10pt">Our approach was built around
two concepts that make it applicable to a wide range of data types and computational tools. The first concept is a classification of data into two broad categories. The second concept is a strategy for localization of data using anchors (hence Method of Anchored
Distributions, or MAD) that captures the relevant information contained in a local or non-local process, respectively, and presents it in terms relevant for the parameter field(s). </span></p>
<p>We have constructed a modular computational platform, with 2 distinct modules, which deliver the capabilities necessary to support the aforementioned concepts: namely, a pre-processing block and a post-processing block. The modules comprise the MAD kernel.
The design of the user-interface allows users and developers to easily link the kernel with their models via a driver. Many of the decisions over assumptions to be adopted or computational tools to be employed are at the user’s discretion and are not
hard-wired into the kernel. The kernel is built using open-source architecture.</p>
<p><span style="font-size:10pt">Eventually, two versions of MAD will be available: a super-computing version (for large-scale applications - in development) and a desktop application (for educational purposes and smaller-scale applications). Also, the kernel
will be eventually interoperable with a wider array of community-based resources (such as CUAHSI’s hydrologic information systems) and is already interoperable with some libraries (such as R). With this framework for generic application of the
concepts and principles of MAD in place, further types of data can be introduced, simplifying assumptions can be added or removed at the user’s discretion, and powerful numerical models and analytical tools, current or under development, can be integrated
much more easily in future analyses under long-term community-based development to follow. </span></p>
<p> </p>
</div><div class="ClearBoth"></div>frystackaSun, 01 May 2016 20:08:24 GMTUpdated Wiki: Home 20160501080824PSource code checked in, #5efba11e88736094223d6fd8cd70ed5216c33b79http://mad.codeplex.com/SourceControl/changeset/5efba11e88736094223d6fd8cd70ed5216c33b79R Based libraries re-packaged for Condor parallel calculation changhongMon, 21 Mar 2016 05:29:59 GMTSource code checked in, #5efba11e88736094223d6fd8cd70ed5216c33b79 20160321052959ASource code checked in, #4f51244b63020962e76c94e23a9dc285edbac305http://mad.codeplex.com/SourceControl/changeset/4f51244b63020962e76c94e23a9dc285edbac305The format simplification of exec.bat in MIN3P changhongSat, 19 Mar 2016 05:32:02 GMTSource code checked in, #4f51244b63020962e76c94e23a9dc285edbac305 20160319053202ASource code checked in, #230de5cd402a3174558206718e3c8c2ca3796ec0http://mad.codeplex.com/SourceControl/changeset/230de5cd402a3174558206718e3c8c2ca3796ec0update for MIN3P changhongFri, 18 Mar 2016 03:40:30 GMTSource code checked in, #230de5cd402a3174558206718e3c8c2ca3796ec0 20160318034030AUpdated Wiki: Documentationhttps://mad.codeplex.com/documentation?version=72<div class="wikidoc">
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=All%20Topics&referringTitle=What%20is%20MAD%3f">Click here for the MAD# wiki </a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=How%20do%20I%20install%20MAD%3f&referringTitle=All%20Topics" target="_blank">Requirements and installation</a></strong></h3>
<h3><strong><a href="https://mad.codeplex.com/wikipage?title=Compiling%20MAD%20-%20Without%20Visual%20Studio">Compiling MAD#</a></strong></h3>
<h2><a href="https://mad.codeplex.com/wikipage?title=Contact"><strong>Contact us</strong></a></h2>
<p> </p>
<p><strong>MAD Papers:</strong></p>
<p>To view the published open abstracts, go to <a href="http://dx.doi.org">http://dx.doi.org</a> and enter the DOI.<br>
</p>
<p><strong>RECENTLY SUBMITTED ARTICLES<br>
</strong></p>
<p>Osorio-Murrilo, C., H. Savoy, and Y. Rubin. “Managing forward models in an inverse modeling framework through a master driver approach.” 2016. Submitted to
<em>Computers and Geosciences. </em></p>
<div title="Page 2">
<div>
<div>
<p>Wang, C., H. Savoy, C. Osoirio-Murillo, X. Li, H. Zhu, and Y. Rubin. “Bayesian inverse modeling for probabilistic site characterization of shallow soil-tunneling system.” 2015. Submitted to
<em>Mathematical Geology</em>. </p>
</div>
</div>
</div>
<p> </p>
<p><strong>PUBLISHED ARTICLES</strong></p>
<p><strong>2015</strong></p>
<p>Heße, F., H. Savoy, C. A. Osorio-Murillo, J. Sege, S. Attinger, and Y. Rubin (2015), Characterizing the impact of roughness and connectivity features of aquifer conductivity using Bayesian inversion,
<em>J. Hydrol.</em>, <em>531</em>, 73–87, doi:10.1016/j.jhydrol.2015.09.067.</p>
<p>The conductivity of an aquifer is usually difficult to represent in a precise manner due to having both a high degree of spatial variability combined with a scarcity of information. As a result, the conductivity is commonly modeled as a spatial random field,
defined by its expected value and a covariance function. This covariance is usually parameterized by fitting a model function to experimental data derived from point measurements of the conductivity. In this study, we investigated the two features that are
often difficult to discern when using such classic characterization schemes: roughness and connectivity. These two features both share the fact that, based on point measurements of the conductivity alone, they are difficult to discern. It therefore stands
to rea- son that the use of additional data could alleviate this problem. To that end, we used the Method of Anchored Distributions (MAD), which is a novel Bayesian tool for the inverse characterization of spatial random fields. MAD is versatilewith respect
to the used data, it has a modular structure and it does not assume any formal relationship between the target variable, i.e. the log hydraulic conductivity, and the data used for the inversion process, e.g. head measurements, draw-down from pumping tests
or breakthrough curves. With respect to the characterization of the aforementioned features, we investigated the impact of several factors, such as different data types on the characterization process. Our findings suggest that both roughness and connectivity
have only a limited impact on flow predictions, suggesting that the choice of such features is of lesser relevance if one is mainly interested in flow simulations. In case of roughness, this limited sensitivity was also seen for transport predictions if the
solute had already travelled some distance. Connectivity however, can be a decisive factor for such simulations.</p>
<p> </p>
<p>Osorio-Murillo, C. A., M. W. Over, H. Savoy, D. P. Ames, and Y. Rubin (2015), Software framework for inverse modeling and uncertainty characterization,
<em>Environ. Model. Softw.</em>, <em>66</em>, 98–109, doi:10.1016/j.envsoft.2015.01.002.</p>
<p>Estimation of spatial random fields (SRFs) is required for predicting groundwater flow, subsurface contaminant movement, and other areas of environmental and earth sciences modeling. This paper presents an inverse modeling framework called MAD# for characterizing
SRFs, which is an imple- mentation of the Bayesian inverse modeling techniqueMethod of Anchored Distributions (MAD).MAD# allowsmodelers to “wrap” simulation models using an extensible driver architecture that exposes model parameters to the inversion
engine. MAD# is implemented in an open source software package with the goal of lowering the barrier to using inverse modeling in education, research, and resource management. MAD# includes an intentionally simple user interface for simulation configuration,
external software integration, spatial domain and model output visualization, and evaluation of model convergence. Four test cases are presented demonstrating the novel functionality of this framework to apply inversion in order to calibrate the model parameters
characterizing a groundwater aquifer</p>
<p> </p>
<p><strong>2010-2014</strong></p>
<p>Over, M. W., X. Chen, Y. Yang, and Y. Rubin (2013), A strategy for improved computational efficiency of the method of anchored distributions, <em>Water Resour. Res.,</em> 49, 1-19, doi: 10.1002/wrcr.20182.</p>
<p>Y. Yang, M. Over, and Y. Rubin (2012) Strategic placement of localization devices (such as pilot points and anchors) in inverse modeling schemes,
<em>Water Resour. Res., </em>48, W08519, doi: 10.1029/2012WR011864.</p>
<p>X. Chen, H. Murakami, M. S. Hahn, G. E. Hammond, M. L. Rockhold, J. M. Zachara, and Y. Rubin (2012) Three-dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data,
<em>Water Resour. Res.,</em> 48, W06501, doi: 10.1029/2011WR010675.</p>
<p>H. Murakami, X. Chen, M. S. Hahn, Y. Liu, M. L. Rockhold, V. R. Vermeul, J. M. Zachara, and Y. Rubin (2010) Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area,<em> Hydrol. Eart Syst. Sci.</em>, 14, doi: 10.5194/hess-14-1989-2010.</p>
<p>Rubin, Y., X. Chen, H. Murakami, and M. Hahn (2010), A Bayesian approach for inverse modeling, data assimilation,and conditional simulation of spatial random fields,
<em>Water Resour. Res.</em>, 46, W10523, doi:10.1029/2009WR008799.</p>
<p> </p>
<p><strong>MAD Abstracts:</strong></p>
<ul>
<li>Rubin Y., et al. 2011 AGU Conference. "<a title="Demonstrating a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282175">Demonstrating
a Multi-purpose, Modular, Open Source Inverse Modeling Plug-in for HydroDesktop on a Simple 2-D Application</a>".
<ul>
<li>December 6, 2011 Afternoon Session </li></ul>
</li><li>Over M.W., et al. 2011 CUAHSI Conference. "<a title="Bringing Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)" href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282176">Bringing
Inverse Modeling to the Scientific Community - Hydrologic Data and the Method of Anchored Distributions (MAD)</a>".
</li><li>Ames D., et al. 2011 AGU Conference. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=283227">An Inverse Modeling Plugin for HydroDesktop using the Method of Anchored Distributions (MAD)</a>"
</li></ul>
<p><strong>MAD Presentation(s):</strong></p>
<ul>
<li>Slides from Over M. W., et al. 2011 CUAHSI Conference presentation. "<a href="http://download.codeplex.com/Download?ProjectName=mad&DownloadId=282177">Bringing Inverse Modeling to the Scientific Community</a>"
</li><li>Slides from Ames D. P., et al. 2012 AGU Fall Meeting presentation. "<a href="http://download-codeplex.sec.s-msft.com/Download?ProjectName=mad&DownloadId=611282">A Modular GIS-based Software Architecture for Model Parameter Estimation using the Method of
Anchored Distributions (MAD)</a>" </li><li>http://www.youtube.com/watch?v=IqWM9Utr9zo </li></ul>
<p><strong>Archived Software Editions and Documentation:</strong></p>
<ul>
<li><a href="http://mad.codeplex.com/wikipage?title=Archived%20Material">Developmental Editions Material</a>
</li></ul>
<p>--------------------</p>
<p><a href="http://mad.codeplex.com/wikipage?title=Bundling">WRR Paper #2012WR011957R Supporting Material</a></p>
</div><div class="ClearBoth"></div>frystackaFri, 04 Mar 2016 04:19:09 GMTUpdated Wiki: Documentation 20160304041909AUpdated Wiki: Homehttps://mad.codeplex.com/wikipage?version=21<div class="wikidoc">
<h2 style="text-align:center"><strong>The Method of Anchored Distributions (MAD): Principles and Implementation as a Community Resource</strong></h2>
<p>In 2010, we proposed to develop a community-based, open-source computational platform for Bayesian characterization of uncertain parameter fields. It was motivated by the need to provide easily-accessible tools for applications and a platform for broad-based,
long-term development, built to meet the challenges brought upon by the ever increasing complexity of modern computational tools, the diversity of data types and the breadth and depth of the subject matters needed for applications. The initiation of such an
effort was intended to emulate the success experienced by other communities with a long tradition of community-based development efforts, and our hope was that it could lead to a transformative change in hydrology. The absence of such an approach had been
hurting the science discovery process. </p>
<p>Our project was to construct a modular, open-source computational platform for application and testing of the general statistical framework for parameter field characterization. <span style="font-size:10pt">Our approach was built around
two concepts that make it applicable to a wide range of data types and computational tools. The first concept is a classification of data into two broad categories. The second concept is a strategy for localization of data using anchors (hence Method of Anchored
Distributions, or MAD) that captures the relevant information contained in a local or non-local process, respectively, and presents it in terms relevant for the parameter field(s). </span></p>
<p>We have constructed a modular computational platform, with 2 distinct modules, which deliver the capabilities necessary to support the aforementioned concepts: namely, a pre-processing block and a post-processing block. The modules comprise the MAD kernel.
The design of the user-interface allows users and developers to easily link the kernel with their models via a driver. Many of the decisions over assumptions to be adopted or computational tools to be employed are at the user’s discretion and are not
hard-wired into the kernel. The kernel is built using open-source architecture.</p>
<p><span style="font-size:10pt">Eventually, two versions of MAD will be available: a super-computing version (for large-scale applications - in development) and a desktop application (for educational purposes and smaller-scale applications). Also, the kernel
will be eventually interoperable with a wider array of community-based resources (such as CUAHSI’s hydrologic information systems) and is already interoperable with some libraries (such as R). With this framework for generic application of the
concepts and principles of MAD in place, further types of data can be introduced, simplifying assumptions can be added or removed at the user’s discretion, and powerful numerical models and analytical tools, current or under development, can be integrated
much more easily in future analyses under long-term community-based development to follow. </span></p>
<p>We have developed and secured a plan for the continuity of this effort after the development phase of the project is concluded. We will deliver the final product to CUAHSI. CUAHSI has agreed to act as the custodian of the MAD computational platform. This
implies securing the integrity of the kernel, updating it and making it available to users and developers. This effort is in line with CUAHSI overall strategy, and CUAHSI has the infrastructure to see this goal carried out. </p>
</div><div class="ClearBoth"></div>frystackaFri, 04 Mar 2016 04:11:08 GMTUpdated Wiki: Home 20160304041108ASource code checked in, #91f4d431bdfe04dfb403d25fafcf539cdcf34704http://mad.codeplex.com/SourceControl/changeset/91f4d431bdfe04dfb403d25fafcf539cdcf34704abnormal likelihood value disposal in perSample.txt changhongTue, 05 Jan 2016 08:24:36 GMTSource code checked in, #91f4d431bdfe04dfb403d25fafcf539cdcf34704 20160105082436ASource code checked in, #25f4b3f3ea297012553ff1b52b269a13d6a4bb1dhttp://mad.codeplex.com/SourceControl/changeset/25f4b3f3ea297012553ff1b52b269a13d6a4bb1dAdd maximum entropy for likelihood value calculation changhongTue, 05 Jan 2016 06:44:02 GMTSource code checked in, #25f4b3f3ea297012553ff1b52b269a13d6a4bb1d 20160105064402ASource code checked in, #bd510fb7e8c4fdc0bb4d3f355dcc1bc8ecdaa14ahttp://mad.codeplex.com/SourceControl/changeset/bd510fb7e8c4fdc0bb4d3f355dcc1bc8ecdaa14aMerge branch 'master' of https://git01.codeplex.com/mad carosoisuMon, 21 Dec 2015 21:06:33 GMTSource code checked in, #bd510fb7e8c4fdc0bb4d3f355dcc1bc8ecdaa14a 20151221090633PSource code checked in, #2599476ca5734aa6b11e75b27a664c14e2d687adhttp://mad.codeplex.com/SourceControl/changeset/2599476ca5734aa6b11e75b27a664c14e2d687adupdate carosoisuMon, 21 Dec 2015 21:06:05 GMTSource code checked in, #2599476ca5734aa6b11e75b27a664c14e2d687ad 20151221090605PSource code checked in, #9d078d3782e6d67d834692aca6843c6d616ee087http://mad.codeplex.com/SourceControl/changeset/9d078d3782e6d67d834692aca6843c6d616ee087Merge branch 'master' of https://git01.codeplex.com/mad Conflicts: 	MPDS/MPDS.csproj changhongWed, 16 Dec 2015 10:29:09 GMTSource code checked in, #9d078d3782e6d67d834692aca6843c6d616ee087 20151216102909ASource code checked in, #fa1eff3e1b719e71aeead63353037a9d43d66800http://mad.codeplex.com/SourceControl/changeset/fa1eff3e1b719e71aeead63353037a9d43d66800FLac3D changhongWed, 16 Dec 2015 10:19:58 GMTSource code checked in, #fa1eff3e1b719e71aeead63353037a9d43d66800 20151216101958ASource code checked in, #a21170adfa94bff746db0051223d30ebdd590bf5http://mad.codeplex.com/SourceControl/changeset/a21170adfa94bff746db0051223d30ebdd590bf5Merge branch 'master' of https://git01.codeplex.com/mad carosoisuMon, 14 Dec 2015 23:02:55 GMTSource code checked in, #a21170adfa94bff746db0051223d30ebdd590bf5 20151214110255PSource code checked in, #45091dac153a37032bf378a0a65d72396b1ea2a5http://mad.codeplex.com/SourceControl/changeset/45091dac153a37032bf378a0a65d72396b1ea2a5update carosoisuMon, 14 Dec 2015 22:34:33 GMTSource code checked in, #45091dac153a37032bf378a0a65d72396b1ea2a5 20151214103433PSource code checked in, #dc3dc04425e4c081669168948332c2a81023bb43http://mad.codeplex.com/SourceControl/changeset/dc3dc04425e4c081669168948332c2a81023bb43Merge branch 'master' of https://git01.codeplex.com/mad hsavoySun, 06 Dec 2015 23:01:07 GMTSource code checked in, #dc3dc04425e4c081669168948332c2a81023bb43 20151206110107P