語系:
繁體中文
English
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
A Bayesian approach to earthquake so...
~
Minson, Sarah.
FindBook
Google Book
Amazon
博客來
A Bayesian approach to earthquake source studies.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
A Bayesian approach to earthquake source studies./
作者:
Minson, Sarah.
面頁冊數:
167 p.
附註:
Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:
Dissertation Abstracts International75-01B(E).
標題:
Geophysics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597166
ISBN:
9781303448775
A Bayesian approach to earthquake source studies.
Minson, Sarah.
A Bayesian approach to earthquake source studies.
- 167 p.
Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Thesis (Ph.D.)--California Institute of Technology, 2010.
Bayesian sampling has several advantages over conventional optimization approaches to solving inverse problems. It produces the distribution of all possible models sampled proportionally to how much each model is consistent with the data and the specified prior information, and thus images the entire solution space, revealing the uncertainties and trade-offs in the model. Bayesian sampling is applicable to both linear and non-linear modeling, and the values of the model parameters being sampled can be constrained based on the physics of the process being studied and do not have to be regularized. However, these methods are computationally challenging for high-dimensional problems.
ISBN: 9781303448775Subjects--Topical Terms:
535228
Geophysics.
A Bayesian approach to earthquake source studies.
LDR
:03139nam a2200301 4500
001
1965601
005
20141030134124.5
008
150210s2010 ||||||||||||||||| ||eng d
020
$a
9781303448775
035
$a
(MiAaPQ)AAI3597166
035
$a
AAI3597166
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Minson, Sarah.
$3
2102279
245
1 2
$a
A Bayesian approach to earthquake source studies.
300
$a
167 p.
500
$a
Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500
$a
Adviser: Mark Simons.
502
$a
Thesis (Ph.D.)--California Institute of Technology, 2010.
520
$a
Bayesian sampling has several advantages over conventional optimization approaches to solving inverse problems. It produces the distribution of all possible models sampled proportionally to how much each model is consistent with the data and the specified prior information, and thus images the entire solution space, revealing the uncertainties and trade-offs in the model. Bayesian sampling is applicable to both linear and non-linear modeling, and the values of the model parameters being sampled can be constrained based on the physics of the process being studied and do not have to be regularized. However, these methods are computationally challenging for high-dimensional problems.
520
$a
Until now the computational expense of Bayesian sampling has been too great for it to be practicable for most geophysical problems. I present a new parallel sampling algorithm called CATMIP for Cascading Adaptive Tempered Metropolis In Parallel. This technique, based on Transitional Markov chain Monte Carlo, makes it possible to sample distributions in many hundreds of dimensions, if the forward model is fast, or to sample computationally expensive forward models in smaller numbers of dimensions. The design of the algorithm is independent of the model being sampled, so CATMIP can be applied to many areas of research.
520
$a
I use CATMIP to produce a finite fault source model for the 2007 Mw 7.7 Tocopilla, Chile earthquake. Surface displacements from the earthquake were recorded by six interferograms and twelve local high-rate GPS stations. Because of the wealth of near-fault data, the source process is well-constrained. I find that the near-field high-rate GPS data have significant resolving power above and beyond the slip distribution determined from static displacements. The location and magnitude of the maximum displacement are resolved. The rupture almost certainly propagated at sub-shear velocities. The full posterior distribution can be used not only to calculate source parameters but also to determine their uncertainties. So while kinematic source modeling and the estimation of source parameters is not new, with CATMIP I am able to use Bayesian sampling to determine which parts of the source process are well-constrained and which are not.
590
$a
School code: 0037.
650
4
$a
Geophysics.
$3
535228
650
4
$a
Applied Mathematics.
$3
1669109
690
$a
0373
690
$a
0364
710
2
$a
California Institute of Technology.
$b
Geophysics.
$3
2102230
773
0
$t
Dissertation Abstracts International
$g
75-01B(E).
790
$a
0037
791
$a
Ph.D.
792
$a
2010
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597166
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9260600
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入