東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Subsurface characterization using ge...

Hou, Zhangshuan.

FindBook

Google Book

Amazon

博客來

Subsurface characterization using geophysical methods.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Subsurface characterization using geophysical methods./
作者:	Hou, Zhangshuan.
面頁冊數:	159 p.
附註:	Adviser: Yoram Rubin.
Contained By:	Dissertation Abstracts International68-02B.
標題:	Engineering, Environmental. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3254291

Subsurface characterization using geophysical methods.
Hou, Zhangshuan.

Subsurface characterization using geophysical methods. - 159 p.

Adviser: Yoram Rubin.

Thesis (Ph.D.)--University of California, Berkeley, 2006.

This dissertation presents research on hydrogeological inversion methodologies for subsurface characterization using geophysical techniques. The study is divided into three parts. In the first study, a comprehensive stochastic approach for forward modeling of the state variables in the vadose zone, as well as for inverse modeling of the hydraulic parameters, is proposed and applied to a field site in Napa Valley, California. The approach combines the Richards equation and the van-Genuchten-Mualem soil models, with initial and boundary conditions provided by geophysical and meteorological measurements, into a Bayesian formalism, which allows integration between different complementary information sources. A major challenge in such studies is the integration between different types of data, of different quality and resolution, as well as with prior information. Accurate modeling of prior information is challenging because of the need to avoid subjective judgment with regard to the quality of the prior information and its significance. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of Minimum Relative Entropy (MRE) is employed. It can often be the case that information that is considered a suitable prior, may in fact become incompatible with field observations as more observations become available. This study considers such a possibility and explores possible indications for prior incompatibility. The forward and inverse modeling approaches are tested using field data and results indicate that the approach is consistent in the sense that as additional data is introduced, the simulated moisture content profile better matches the observed profile, and the predictive intervals become much narrower. The narrower intervals of the soil moisture predictions indicate the reduction of uncertainties associated with the estimated model parameters.Subjects--Topical Terms:

783782
Engineering, Environmental.

Subsurface characterization using geophysical methods.
LDR:05200nam 2200289 a 45 001 954235
005 20110621
008 110622s2006 eng d
035 $a (UMI)AAI3254291
035 $a AAI3254291
040 $a UMI $c UMI
100 1 $a Hou, Zhangshuan. $3 1277708
245 1 0 $a Subsurface characterization using geophysical methods.
300 $a 159 p.
500 $a Adviser: Yoram Rubin.
500 $a Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 1212.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2006.
520 $a This dissertation presents research on hydrogeological inversion methodologies for subsurface characterization using geophysical techniques. The study is divided into three parts. In the first study, a comprehensive stochastic approach for forward modeling of the state variables in the vadose zone, as well as for inverse modeling of the hydraulic parameters, is proposed and applied to a field site in Napa Valley, California. The approach combines the Richards equation and the van-Genuchten-Mualem soil models, with initial and boundary conditions provided by geophysical and meteorological measurements, into a Bayesian formalism, which allows integration between different complementary information sources. A major challenge in such studies is the integration between different types of data, of different quality and resolution, as well as with prior information. Accurate modeling of prior information is challenging because of the need to avoid subjective judgment with regard to the quality of the prior information and its significance. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of Minimum Relative Entropy (MRE) is employed. It can often be the case that information that is considered a suitable prior, may in fact become incompatible with field observations as more observations become available. This study considers such a possibility and explores possible indications for prior incompatibility. The forward and inverse modeling approaches are tested using field data and results indicate that the approach is consistent in the sense that as additional data is introduced, the simulated moisture content profile better matches the observed profile, and the predictive intervals become much narrower. The narrower intervals of the soil moisture predictions indicate the reduction of uncertainties associated with the estimated model parameters.
520 $a In the second study, a joint inversion approach for estimating reservoir fluid saturations and porosity is proposed. The approach couples seismic amplitude versus angle (AVA) and marine controlled source electromagnetic (CSEM) forward models into a Bayesian framework, which allows for integration of complementary information. Instead of single-valued estimates provided by deterministic methods, the approach gives a probability distribution for unknown parameters of interest, such as reservoir fluid saturations or porosity at various locations. The distribution means, modes, and confidence intervals can be calculated, providing a more complete understanding of the uncertainty in the parameter estimates. The approach is demonstrated using synthetic and field data sets. Results show that joint inversion using seismic and EM data gives better estimates of reservoir parameters than estimates from either geophysical data set used in isolation. Moreover, a more informative prior leads to much narrower predictive intervals of the target parameters, with mean values of the posterior distributions closer to logged values.
520 $a In the third study, we investigate the applicability and limitations of two types of ray-tracing models, the curved-ray and straight-ray methods, through forward and inverse modeling studies, and provide guidelines for applications when using tomographic ground-penetrating-radar (GPR) for subsurface characterization. In the forward modeling study, we compare GPR traveltime calculations between the two ray-tracing methods under various flow conditions, and study the sensitivity of the differences in the first-arrival traveltime calculations to factors including geometry, heterogeneity and data acquisition parameters. In the inverse modeling study, we compare the effects of the differences between these two methods on the quality of the inversion. Results show that the straight-ray model, which is computationally more efficient, may work as an alternative of the curved-ray model for shallow subsurface characterization when the soil is relatively sandy. It is also favorable for the straight-ray model when hydraulic conductivity has layered heterogeneity or has similar spatial patterns in both vertical and horizontal directions. However, the applicability of the straight-ray model may be limited when the distance between the boreholes is large compared to their depths.
590 $a School code: 0028.
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Hydrology. $3 545716
690 $a 0388
690 $a 0775
710 2 $a University of California, Berkeley. $3 687832
773 0 $t Dissertation Abstracts International $g 68-02B.
790 $a 0028
790 1 0 $a Rubin, Yoram, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3254291