Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information

Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xiao, Bo [verfasserIn]
Körperschaften:	Eberhard Karls Universität Tübingen [Grad-verleihende Institution]
Hochschulschrift:	Dissertation ; Eberhard Karls Universität Tübingen ; 2021

Format:	E-Book
Sprache:	Englisch

Erschienen:	Tübingen: 2021

Schlagwörter:	Hydrogeologie
Formangabe:	Hochschulschrift

Umfang:	1 Online-Ressource (xii, 155 Seiten) ; Illustrationen

Weitere Ausgabe:	Erscheint auch als Druck-Ausgabe Xiao, Bo: Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information - Tübingen, 2021

Links:	Link aufrufen Volltext Volltext Volltext Volltext Link aufrufen Link aufrufen

DOI / URN:	urn:nbn:de:bsz:21-dspace-1155070 10.15496/publikation-56882

Katalog-ID:	180574352X

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title_sort	three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information
title_auth	Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information
abstract	Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K.
abstractGer	Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K.
abstract_unstemmed	Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K.
url	https://publikationen.uni-tuebingen.de/xmlui/handle/10900/115507/ http://hdl.handle.net/10900/115507 http://dx.doi.org/10.15496/publikation-56882 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 https://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1155070 https://d-nb.info/1260377628/34
author2	Eberhard Karls Universität Tübingen
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allfields	urn:nbn:de:bsz:21-dspace-1155070 urn 10.15496/publikation-56882 doi 10900/115507 hdl (DE-627)180574352X (DE-599)KXP180574352X (OCoLC)1322446292 DE-627 ger DE-627 rda eng XA-DE-BW 551.577 DE-101 550 DE-101 Xiao, Bo verfasserin (DE-588)1259003175 (DE-627)1805743406 aut Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information vorgelegt von Bo Xiao Tübingen 2021 1 Online-Ressource (xii, 155 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Eberhard Karls Universität Tübingen 2021 Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K. (DE-588)4026307-1 (DE-627)104689129 (DE-576)208965998 Hydrogeologie gnd Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eberhard Karls Universität Tübingen Grad-verleihende Institution (DE-588)36187-2 (DE-627)100833349 (DE-576)190344806 dgg Tübingen (DE-588)4061147-4 (DE-627)106136933 (DE-576)209138351 uvp Erscheint auch als Druck-Ausgabe Xiao, Bo Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information Tübingen, 2021 xii, 155 Seiten (DE-627)1805743562 https://publikationen.uni-tuebingen.de/xmlui/handle/10900/115507/ Verlag kostenfrei Volltext http://hdl.handle.net/10900/115507 2022-07-06 Resolving-System kostenfrei Volltext http://dx.doi.org/10.15496/publikation-56882 2022-07-06 Resolving-System kostenfrei Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System https://d-nb.info/1260377628/34 2022-07-06 Langzeitarchivierung Nationalbibliothek GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2001 ISIL_DE-21 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.577 20 01 0084 4162684081 x 08-07-22 21 01 0046 4162703043 z 08-07-22 22 01 0018 4162721807 SUBolrd xu 08-07-22 23 01 0830 4162740011 olr-d x 08-07-22 30 01 0104 4162752362 z 08-07-22 40 01 0007 4162761213 xsn 08-07-22 60 01 0705 416277899X OLRD z 08-07-22 63 01 3401 4162796424 ORD x 08-07-22 65 01 0003 4169881788 GBV-ODiss Open Access z 18-07-22 70 01 0089 4162805237 zdo 08-07-22 105 01 0841 4162905258 z 08-07-22 110 01 3110 416281693X x 08-07-22 132 01 0959 416282682X OLR-DISS x 08-07-22 151 01 0546 4162844321 OLR-ODISS z 08-07-22 161 01 0960 416285033X ORD z 08-07-22 293 01 3293 4162893268 ORD z 08-07-22 370 01 4370 4162903018 x 08-07-22 2001 01 DE-21 4142494538 00 --%%-- --%%-- --%%-- --%%-- Elektronischer Volltext - Zugang über WWW l01 02-06-22 2403 01 DE-LFER 4186044597 00 --%%-- --%%-- n --%%-- l01 08-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 21 01 0046 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 22 01 0018 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 23 01 0830 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 30 01 0104 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 40 01 0007 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 60 01 0705 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 LF 65 01 0003 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 70 01 0089 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 105 01 0841 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 110 01 3110 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 132 01 0959 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 161 01 0960 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 293 01 3293 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 370 01 4370 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2403 01 DE-LFER http://dx.doi.org/10.15496/publikation-56882 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 00 s Non-Multi-Gaussian 2001 01 DE-21 01 s Stochastic simulation 2001 01 DE-21 02 s Geostatistics 2001 01 DE-21 03 s Copula 2001 01 DE-21 04 s Solute transport 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 65 01 0003 GBV-ODiss 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-07-08:11:02:55
spelling	urn:nbn:de:bsz:21-dspace-1155070 urn 10.15496/publikation-56882 doi 10900/115507 hdl (DE-627)180574352X (DE-599)KXP180574352X (OCoLC)1322446292 DE-627 ger DE-627 rda eng XA-DE-BW 551.577 DE-101 550 DE-101 Xiao, Bo verfasserin (DE-588)1259003175 (DE-627)1805743406 aut Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information vorgelegt von Bo Xiao Tübingen 2021 1 Online-Ressource (xii, 155 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Eberhard Karls Universität Tübingen 2021 Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K. (DE-588)4026307-1 (DE-627)104689129 (DE-576)208965998 Hydrogeologie gnd Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eberhard Karls Universität Tübingen Grad-verleihende Institution (DE-588)36187-2 (DE-627)100833349 (DE-576)190344806 dgg Tübingen (DE-588)4061147-4 (DE-627)106136933 (DE-576)209138351 uvp Erscheint auch als Druck-Ausgabe Xiao, Bo Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information Tübingen, 2021 xii, 155 Seiten (DE-627)1805743562 https://publikationen.uni-tuebingen.de/xmlui/handle/10900/115507/ Verlag kostenfrei Volltext http://hdl.handle.net/10900/115507 2022-07-06 Resolving-System kostenfrei Volltext http://dx.doi.org/10.15496/publikation-56882 2022-07-06 Resolving-System kostenfrei Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System https://d-nb.info/1260377628/34 2022-07-06 Langzeitarchivierung Nationalbibliothek GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2001 ISIL_DE-21 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.577 20 01 0084 4162684081 x 08-07-22 21 01 0046 4162703043 z 08-07-22 22 01 0018 4162721807 SUBolrd xu 08-07-22 23 01 0830 4162740011 olr-d x 08-07-22 30 01 0104 4162752362 z 08-07-22 40 01 0007 4162761213 xsn 08-07-22 60 01 0705 416277899X OLRD z 08-07-22 63 01 3401 4162796424 ORD x 08-07-22 65 01 0003 4169881788 GBV-ODiss Open Access z 18-07-22 70 01 0089 4162805237 zdo 08-07-22 105 01 0841 4162905258 z 08-07-22 110 01 3110 416281693X x 08-07-22 132 01 0959 416282682X OLR-DISS x 08-07-22 151 01 0546 4162844321 OLR-ODISS z 08-07-22 161 01 0960 416285033X ORD z 08-07-22 293 01 3293 4162893268 ORD z 08-07-22 370 01 4370 4162903018 x 08-07-22 2001 01 DE-21 4142494538 00 --%%-- --%%-- --%%-- --%%-- Elektronischer Volltext - Zugang über WWW l01 02-06-22 2403 01 DE-LFER 4186044597 00 --%%-- --%%-- n --%%-- l01 08-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 21 01 0046 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 22 01 0018 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 23 01 0830 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 30 01 0104 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 40 01 0007 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 60 01 0705 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 LF 65 01 0003 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 70 01 0089 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 105 01 0841 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 110 01 3110 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 132 01 0959 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 161 01 0960 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 293 01 3293 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 370 01 4370 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2403 01 DE-LFER http://dx.doi.org/10.15496/publikation-56882 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 00 s Non-Multi-Gaussian 2001 01 DE-21 01 s Stochastic simulation 2001 01 DE-21 02 s Geostatistics 2001 01 DE-21 03 s Copula 2001 01 DE-21 04 s Solute transport 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 65 01 0003 GBV-ODiss 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-07-08:11:02:55
allfields_unstemmed	urn:nbn:de:bsz:21-dspace-1155070 urn 10.15496/publikation-56882 doi 10900/115507 hdl (DE-627)180574352X (DE-599)KXP180574352X (OCoLC)1322446292 DE-627 ger DE-627 rda eng XA-DE-BW 551.577 DE-101 550 DE-101 Xiao, Bo verfasserin (DE-588)1259003175 (DE-627)1805743406 aut Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information vorgelegt von Bo Xiao Tübingen 2021 1 Online-Ressource (xii, 155 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Eberhard Karls Universität Tübingen 2021 Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K. (DE-588)4026307-1 (DE-627)104689129 (DE-576)208965998 Hydrogeologie gnd Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eberhard Karls Universität Tübingen Grad-verleihende Institution (DE-588)36187-2 (DE-627)100833349 (DE-576)190344806 dgg Tübingen (DE-588)4061147-4 (DE-627)106136933 (DE-576)209138351 uvp Erscheint auch als Druck-Ausgabe Xiao, Bo Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information Tübingen, 2021 xii, 155 Seiten (DE-627)1805743562 https://publikationen.uni-tuebingen.de/xmlui/handle/10900/115507/ Verlag kostenfrei Volltext http://hdl.handle.net/10900/115507 2022-07-06 Resolving-System kostenfrei Volltext http://dx.doi.org/10.15496/publikation-56882 2022-07-06 Resolving-System kostenfrei Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System https://d-nb.info/1260377628/34 2022-07-06 Langzeitarchivierung Nationalbibliothek GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2001 ISIL_DE-21 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.577 20 01 0084 4162684081 x 08-07-22 21 01 0046 4162703043 z 08-07-22 22 01 0018 4162721807 SUBolrd xu 08-07-22 23 01 0830 4162740011 olr-d x 08-07-22 30 01 0104 4162752362 z 08-07-22 40 01 0007 4162761213 xsn 08-07-22 60 01 0705 416277899X OLRD z 08-07-22 63 01 3401 4162796424 ORD x 08-07-22 65 01 0003 4169881788 GBV-ODiss Open Access z 18-07-22 70 01 0089 4162805237 zdo 08-07-22 105 01 0841 4162905258 z 08-07-22 110 01 3110 416281693X x 08-07-22 132 01 0959 416282682X OLR-DISS x 08-07-22 151 01 0546 4162844321 OLR-ODISS z 08-07-22 161 01 0960 416285033X ORD z 08-07-22 293 01 3293 4162893268 ORD z 08-07-22 370 01 4370 4162903018 x 08-07-22 2001 01 DE-21 4142494538 00 --%%-- --%%-- --%%-- --%%-- Elektronischer Volltext - Zugang über WWW l01 02-06-22 2403 01 DE-LFER 4186044597 00 --%%-- --%%-- n --%%-- l01 08-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 21 01 0046 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 22 01 0018 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 23 01 0830 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 30 01 0104 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 40 01 0007 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 60 01 0705 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 LF 65 01 0003 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 70 01 0089 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 105 01 0841 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 110 01 3110 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 132 01 0959 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 161 01 0960 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 293 01 3293 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 370 01 4370 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2403 01 DE-LFER http://dx.doi.org/10.15496/publikation-56882 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 00 s Non-Multi-Gaussian 2001 01 DE-21 01 s Stochastic simulation 2001 01 DE-21 02 s Geostatistics 2001 01 DE-21 03 s Copula 2001 01 DE-21 04 s Solute transport 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 65 01 0003 GBV-ODiss 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-07-08:11:02:55
allfieldsGer	urn:nbn:de:bsz:21-dspace-1155070 urn 10.15496/publikation-56882 doi 10900/115507 hdl (DE-627)180574352X (DE-599)KXP180574352X (OCoLC)1322446292 DE-627 ger DE-627 rda eng XA-DE-BW 551.577 DE-101 550 DE-101 Xiao, Bo verfasserin (DE-588)1259003175 (DE-627)1805743406 aut Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information vorgelegt von Bo Xiao Tübingen 2021 1 Online-Ressource (xii, 155 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Eberhard Karls Universität Tübingen 2021 Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K. (DE-588)4026307-1 (DE-627)104689129 (DE-576)208965998 Hydrogeologie gnd Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eberhard Karls Universität Tübingen Grad-verleihende Institution (DE-588)36187-2 (DE-627)100833349 (DE-576)190344806 dgg Tübingen (DE-588)4061147-4 (DE-627)106136933 (DE-576)209138351 uvp Erscheint auch als Druck-Ausgabe Xiao, Bo Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information Tübingen, 2021 xii, 155 Seiten (DE-627)1805743562 https://publikationen.uni-tuebingen.de/xmlui/handle/10900/115507/ Verlag kostenfrei Volltext http://hdl.handle.net/10900/115507 2022-07-06 Resolving-System kostenfrei Volltext http://dx.doi.org/10.15496/publikation-56882 2022-07-06 Resolving-System kostenfrei Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System https://d-nb.info/1260377628/34 2022-07-06 Langzeitarchivierung Nationalbibliothek GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2001 ISIL_DE-21 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.577 20 01 0084 4162684081 x 08-07-22 21 01 0046 4162703043 z 08-07-22 22 01 0018 4162721807 SUBolrd xu 08-07-22 23 01 0830 4162740011 olr-d x 08-07-22 30 01 0104 4162752362 z 08-07-22 40 01 0007 4162761213 xsn 08-07-22 60 01 0705 416277899X OLRD z 08-07-22 63 01 3401 4162796424 ORD x 08-07-22 65 01 0003 4169881788 GBV-ODiss Open Access z 18-07-22 70 01 0089 4162805237 zdo 08-07-22 105 01 0841 4162905258 z 08-07-22 110 01 3110 416281693X x 08-07-22 132 01 0959 416282682X OLR-DISS x 08-07-22 151 01 0546 4162844321 OLR-ODISS z 08-07-22 161 01 0960 416285033X ORD z 08-07-22 293 01 3293 4162893268 ORD z 08-07-22 370 01 4370 4162903018 x 08-07-22 2001 01 DE-21 4142494538 00 --%%-- --%%-- --%%-- --%%-- Elektronischer Volltext - Zugang über WWW l01 02-06-22 2403 01 DE-LFER 4186044597 00 --%%-- --%%-- n --%%-- l01 08-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 21 01 0046 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 22 01 0018 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 23 01 0830 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 30 01 0104 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 40 01 0007 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 60 01 0705 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 LF 65 01 0003 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 70 01 0089 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 105 01 0841 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 110 01 3110 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 132 01 0959 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 161 01 0960 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 293 01 3293 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 370 01 4370 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2403 01 DE-LFER http://dx.doi.org/10.15496/publikation-56882 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 00 s Non-Multi-Gaussian 2001 01 DE-21 01 s Stochastic simulation 2001 01 DE-21 02 s Geostatistics 2001 01 DE-21 03 s Copula 2001 01 DE-21 04 s Solute transport 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 65 01 0003 GBV-ODiss 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-07-08:11:02:55
allfieldsSound	urn:nbn:de:bsz:21-dspace-1155070 urn 10.15496/publikation-56882 doi 10900/115507 hdl (DE-627)180574352X (DE-599)KXP180574352X (OCoLC)1322446292 DE-627 ger DE-627 rda eng XA-DE-BW 551.577 DE-101 550 DE-101 Xiao, Bo verfasserin (DE-588)1259003175 (DE-627)1805743406 aut Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information vorgelegt von Bo Xiao Tübingen 2021 1 Online-Ressource (xii, 155 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Eberhard Karls Universität Tübingen 2021 Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. A better match between modeled and measured solute transport behavior is obtained when asymmetry is included in the geostatistical model for K. (DE-588)4026307-1 (DE-627)104689129 (DE-576)208965998 Hydrogeologie gnd Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Eberhard Karls Universität Tübingen Grad-verleihende Institution (DE-588)36187-2 (DE-627)100833349 (DE-576)190344806 dgg Tübingen (DE-588)4061147-4 (DE-627)106136933 (DE-576)209138351 uvp Erscheint auch als Druck-Ausgabe Xiao, Bo Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information Tübingen, 2021 xii, 155 Seiten (DE-627)1805743562 https://publikationen.uni-tuebingen.de/xmlui/handle/10900/115507/ Verlag kostenfrei Volltext http://hdl.handle.net/10900/115507 2022-07-06 Resolving-System kostenfrei Volltext http://dx.doi.org/10.15496/publikation-56882 2022-07-06 Resolving-System kostenfrei Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1155070 2022-07-06 Resolving-System https://d-nb.info/1260377628/34 2022-07-06 Langzeitarchivierung Nationalbibliothek GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_65 ISIL_DE-3 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2001 ISIL_DE-21 GBV_ILN_2403 ISIL_DE-LFER BO 045F 551.577 20 01 0084 4162684081 x 08-07-22 21 01 0046 4162703043 z 08-07-22 22 01 0018 4162721807 SUBolrd xu 08-07-22 23 01 0830 4162740011 olr-d x 08-07-22 30 01 0104 4162752362 z 08-07-22 40 01 0007 4162761213 xsn 08-07-22 60 01 0705 416277899X OLRD z 08-07-22 63 01 3401 4162796424 ORD x 08-07-22 65 01 0003 4169881788 GBV-ODiss Open Access z 18-07-22 70 01 0089 4162805237 zdo 08-07-22 105 01 0841 4162905258 z 08-07-22 110 01 3110 416281693X x 08-07-22 132 01 0959 416282682X OLR-DISS x 08-07-22 151 01 0546 4162844321 OLR-ODISS z 08-07-22 161 01 0960 416285033X ORD z 08-07-22 293 01 3293 4162893268 ORD z 08-07-22 370 01 4370 4162903018 x 08-07-22 2001 01 DE-21 4142494538 00 --%%-- --%%-- --%%-- --%%-- Elektronischer Volltext - Zugang über WWW l01 02-06-22 2403 01 DE-LFER 4186044597 00 --%%-- --%%-- n --%%-- l01 08-09-22 20 01 0084 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 21 01 0046 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 22 01 0018 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 23 01 0830 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 30 01 0104 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 40 01 0007 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 60 01 0705 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 63 01 3401 E-Book http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 LF 65 01 0003 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 70 01 0089 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 105 01 0841 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 110 01 3110 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 132 01 0959 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 151 01 0546 Volltext http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 161 01 0960 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 293 01 3293 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 370 01 4370 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2403 01 DE-LFER http://dx.doi.org/10.15496/publikation-56882 2403 01 DE-LFER http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1155070 2001 01 DE-21 00 s Non-Multi-Gaussian 2001 01 DE-21 01 s Stochastic simulation 2001 01 DE-21 02 s Geostatistics 2001 01 DE-21 03 s Copula 2001 01 DE-21 04 s Solute transport 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 65 01 0003 GBV-ODiss 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2022-07-08:11:02:55
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authorswithroles_txt_mv	Xiao, Bo @@aut@@
genre_facet	Hochschulschrift
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a2200265 4500</leader><controlfield tag="001">180574352X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220706220747.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220602s2021 gw \|\|\|\|\|om 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">urn:nbn:de:bsz:21-dspace-1155070</subfield><subfield code="2">urn</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.15496/publikation-56882</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10900/115507</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)180574352X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP180574352X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1322446292</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-DE-BW</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">551.577</subfield><subfield code="q">DE-101</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DE-101</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Xiao, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1259003175</subfield><subfield code="0">(DE-627)1805743406</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information</subfield><subfield code="c">vorgelegt von Bo Xiao</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Tübingen</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xii, 155 Seiten)</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="b">Dissertation</subfield><subfield code="c">Eberhard Karls Universität Tübingen</subfield><subfield code="d">2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Standard geostatistical methods in hydrogeology assume a multi-Gaussian distribution of the log-hydraulic conductivity (K), implying that intermediate values are well connected, embedding isolated zones of high and low values. Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. The non-multi-Gaussian models generate preferential flow paths that lead to a stronger correlation of velocity at large separation distances and consequently larger dispersivities in comparison to the (quasi) multi-Gaussian models. 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doi_str	10.15496/publikation-56882
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Two datasets of hydraulic conductivity K from the MAcroDispersion Experiment (MADE) site in Columbus, Mississippi, are analyzed, one measured by direct-push injection-logging (DPIL) at 31,123 observation points in 58 vertical profiles and the other by flowmeter profiling at 2611 observation points in 67 wells. The analysis is performed using copula techniques that do not rely on the assumption of multivariate Gaussianity and provide a means to characterize differing degrees of spatial dependence in different quantiles of the K distribution. This characterization provides better insights into the similarities and differences between the two datasets. In addition to the marginal distributions and two-point geostatistical measures, copula-based bivariate rank correlation and asymmetry measures are analyzed and compared. Furthermore, the parameter estimates obtained by likelihood estimation using n-point theoretical models are analyzed. This analysis confirms the similarity of the spatial dependence of K between the two datasets in terms of their marginal distributions and bivariate measures, particularly in the vertical direction. Clear indications of non-multi-Gaussian spatial dependence structures of K are found at this site. The estimation of the K distribution can be improved by taking into account either non-Gaussianity or a censoring threshold, which are expected to lead to a more realistic description of processes that depend on K. A framework to generate K-field is used that allows non-multi-Gaussian dependence using the multi-objective phase-annealing (PA) method. One objective is to mimic the “asymmetry” of the measured K that indicates the degree of non-Gaussianity. The K-field at the MADE site is mimicked using both DPIL and flowmeter datasets for conditioning. The differences in data quality between the datasets are considered. As the mean and variance of the two datasets differ, the K fields are conditioned on the measured values of the flowmeter dataset and the order within the DPIL dataset. The degree of non-Gaussianity is quantified by the asymmetry of the copula, which is accounted for in the three-dimensional conditioning procedure using the spectral phase-annealing method. The impacts of including as much information as possible in the conditioning procedure on key solute-transport characteristics are analyzed using the comparison between the non-multiGaussian method with multi-Gaussian geostatistical approaches. As a transport metric, the one- and two-particle spatial moments of solute plumes and the associated dispersivities resulting from particle-tracking random-walk simulations are considered. 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Three-dimensional non-multi-gaussian simulation of hydraulic conductivity including multiple types of information

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