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Advances in parameter estimation for...
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Snodgrass, Mark F.
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Advances in parameter estimation for subsurface processes.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Advances in parameter estimation for subsurface processes./
作者:
Snodgrass, Mark F.
面頁冊數:
189 p.
附註:
Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0170.
Contained By:
Dissertation Abstracts International66-01B.
標題:
Hydrology. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3162322
ISBN:
0496960156
Advances in parameter estimation for subsurface processes.
Snodgrass, Mark F.
Advances in parameter estimation for subsurface processes.
- 189 p.
Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0170.
Thesis (Ph.D.)--Stanford University, 2005.
The dissertation presents three efficient and novel methods of inferring parameters that are of interest to the practicing hydrogeologist and environmental engineer.
ISBN: 0496960156Subjects--Topical Terms:
545716
Hydrology.
Advances in parameter estimation for subsurface processes.
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The dissertation presents three efficient and novel methods of inferring parameters that are of interest to the practicing hydrogeologist and environmental engineer.
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First, a method to evaluate fast- and zero-order in situ reaction rates from a push-pull test is presented. A single-well push-pull test starts with the rapid injection of a slug containing a known quantity of a conservative tracer and a reactive solute into the saturated zone. The slug is periodically extracted and sampled from the same well. For zero- or first-order reactions, in the absence of sorption and assuming negligible background concentrations, these measurements can be used to evaluate reaction rate coefficients directly. The method does not involve computer-based solute transport models and requires no knowledge of hydraulic parameters.
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Second, a geostatistical approach to contaminant source estimation is presented. The problem is to estimate the release history of a conservative solute given point concentration measurements at some time after the release. A Bayesian framework is followed to derive the best estimate and to quantify the estimation error. The relationship between this approach and common regularization and interpolation schemes is discussed. The performance of the method with sparse data and large measurement error is examined. Emphasis is placed on formulating the estimation method in a computationally efficient manner. The issue of model validation is addressed. The method is applied to field data to identify the history of a TCE and PCE plume.
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Finally, I present a geostatistical approach to multi-directional aquifer stimulation in order to better identify the transmissivity field. Hydraulic head measurements---taken at a few locations but under a number of different steady-state flow conditions---are used to estimate the transmissivity. Well installation is generally the most costly aspect of obtaining hydraulic head measurements. Therefore, it is advantageous to obtain as many informative measurements from each sampling location as possible. Simulation provide evidence that a few sampling locations are capable of providing enough information to estimate the transmissivity field. Furthermore, the innovation of, or new information provided by, each measurement can be examined by looking at the corresponding spline and sensitivity matrix.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3162322
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