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         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/21146cdb2cb2120cac1495799520c7522/hermann",         
         "tags" : [
            "Copula;","Inverse","Multiple","mixing;","modeling;","point","statistics}","{Random"
         ],
         
         "intraHash" : "1146cdb2cb2120cac1495799520c7522",
         "interHash" : "ebc1acb5c6a1065a9ad744fc313e15ef",
         "label" : "Random Mixing: An Approach to Inverse Modeling for Groundwater Flow and\n   Transport Problems",
         "user" : "hermann",
         "description" : "",
         "date" : "2017-05-18 11:32:12",
         "changeDate" : "2017-05-18 09:32:12",
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         "pub-type": "article",
         "journal": "TRANSPORT IN POROUS MEDIA","publisher":"SPRINGER","address":"233 SPRING ST, NEW YORK, NY 10013 USA",
         "year": "{2016}", 
         "url": "", 
         
         "author": [ 
            "Andras Bardossy","Sebastian Hoerning"
         ],
         "authors": [
         	
            	{"first" : "Andras",	"last" : "Bardossy"},
            	{"first" : "Sebastian",	"last" : "Hoerning"}
         ],
         "volume": "114","number": "2, SI","pages": "241-259","abstract": "This paper presents a novel methodology for inverse modeling of\n   groundwater flow and transport problems in a Monte Carlo framework,\n   i.e., multiple solutions to the inverse problem are generated. The\n   methodology is based on the concept of random mixing of spatial random\n   fields. The conditional target hydraulic transmissivity field is\n   obtained as a linear combination of unconditional spatial random fields.\n   The corresponding weights of the linear combination are selected such\n   that the spatial variability of the hydraulic transmissivities as well\n   as the actual observed transmissivity values are reproduced. The\n   constraints related to the hydraulic head and contaminant concentration\n   observations are nonlinear. In order to fulfill these constraints, a\n   specific property of the presented approach is used. A connected domain\n   of fields fulfilling all linear constraints is identified. This domain\n   includes an infinite number of realizations, and in this domain, the\n   head and concentration deviations are minimized using standard\n   continuous optimization techniques. The methodology uses spatial copulas\n   to describe the spatial dependence structure. A combination with\n   multiple point statistics allows inversion under specific structural\n   constraints.",
         "author-email" : "andras.bardossy@iws.uni-stuttgart.de\n   sebastian.hoerning@iws.uni-stuttgart.de",
         
         "issn" : "0169-3913",
         
         "keywords-plus" : "GRADUAL DEFORMATION; CONDITIONAL SIMULATION; ITERATIVE CALIBRATION;\n   MEDIA",
         
         "funding-acknowledgement" : "German Science Foundation (DFG) [GRK 1398]",
         
         "research-areas" : "Engineering",
         
         "eissn" : "1573-1634",
         
         "number-of-cited-references" : "28",
         
         "affiliation" : "Horning, S (Reprint Author), Univ Stuttgart, Inst Modeling Hydraul & Environm Syst, Dept Hydrol & Geohydrol, Stuttgart, Germany.\n   Bardossy, Andras; Hoerning, Sebastian, Univ Stuttgart, Inst Modeling Hydraul & Environm Syst, Dept Hydrol & Geohydrol, Stuttgart, Germany.",
         
         "web-of-science-categories" : "Engineering, Chemical",
         
         "language" : "English",
         
         "funding-text" : "Research for this paper was supported by the German Science Foundation\n   (DFG) in the framework of the International Research Training Group\n   NUPUS under Grant No. GRK 1398.",
         
         "times-cited" : "1",
         
         "doi" : "10.1007/s11242-015-0608-4",
         
         "bibtexKey": "ISI:000382094800003"

      }
	  
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