Data integration and inverse methods for characterization of multicomponent hydrologic systems

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This work is motivated by the need for tools that can be used to accurately characterize subsurface environments at a range of scales, and given a range of different data sources. There is a pressing need for such tools, since tuning subsurface models is often the most critical step in making accurate predictions about groundwater flow and transport of contaminants. Increasingly, dense instrumentation at groundwater sites is becoming more common, and a range of different testing methods, both hydrologic (pumping tests, tracer tests) and geophysical (GPR, seismic, electrical resistivity), may be used in order to produce images of subsurface heterogeneity. At sites such as these, key questions include how to integrate all measurements at a site into a single model, how to efficiently and accurately "invert" all data, and how to interpret the meaning of geophysical information. In five separate studies, advancements in inverse modeling and parameter estimation were introduced in order to provide useful tools for solving such subsurface imaging problems. The key contributions of this thesis are as follows 1) Derivation of a generalized, continuous-form adjoint state equation, which accelerates the numerical solution of a range of inverse problems; 2) Application of a simplified groundwater flow model and geostatistical inverse methods to a field problem at the Boise Hydrogeophysical Research Site; 3) Development of a level set-based method for joint inversion of parameter fields dominated by structural features; 4) Development of a maximum likelihood method for estimating petrophysical relations, which aid in the application of joint inversion; and 5) Development of a stochastic method for integration of multiple data sources and optimization of groundwater cleanup operations. Overall, the methods presented in these studies provide novel methods for integrating multiple data sources in order to identify the features of hydrologic systems.


Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2010
Issuance monographic
Language English


Associated with Cardiff, Michael Andrew
Associated with Stanford University, Civil & Environmental Engineering Department
Primary advisor Kitanidis, P. K. (Peter K.)
Thesis advisor Kitanidis, P. K. (Peter K.)
Thesis advisor Freyberg, David L
Thesis advisor Knight, Rosemary (Rosemary Jane), 1953-
Advisor Freyberg, David L
Advisor Knight, Rosemary (Rosemary Jane), 1953-


Genre Theses

Bibliographic information

Statement of responsibility Michael Andrew Cardiff.
Note Submitted to the Department of Civil and Environmental Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2010.
Location electronic resource

Access conditions

© 2010 by Michael Andrew Cardiff

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