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Filter-based training pattern classi...

Zhang, Tuanfeng.

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Filter-based training pattern classification for spatial pattern simulation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Filter-based training pattern classification for spatial pattern simulation./
Author:	Zhang, Tuanfeng.
Description:	137 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0768.
Contained By:	Dissertation Abstracts International67-02B.
Subject:	Geology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3209066
ISBN:	9780542571503

Filter-based training pattern classification for spatial pattern simulation.
Zhang, Tuanfeng.

Filter-based training pattern classification for spatial pattern simulation. - 137 p.

Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0768.

Thesis (Ph.D.)--Stanford University, 2006.

Stochastic simulation of spatial patterns of complex subsurface geological structures is critical for decision making under uncertainty. The challenge is to represent this complexity using training images that incorporate typical local structures and textures, and to derive distributional properties from such training images for purposes of stochastic simulation. The large number of local pattern possibilities requires dimension reduction, then methods for pattern sampling and data conditioning towards simulation require careful attention.

ISBN: 9780542571503Subjects--Topical Terms:

516570
Geology.

Filter-based training pattern classification for spatial pattern simulation.
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100 1 $a Zhang, Tuanfeng. $3 1919262
245 1 0 $a Filter-based training pattern classification for spatial pattern simulation.
300 $a 137 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0768.
500 $a Adviser: Paul Switzer.
502 $a Thesis (Ph.D.)--Stanford University, 2006.
520 $a Stochastic simulation of spatial patterns of complex subsurface geological structures is critical for decision making under uncertainty. The challenge is to represent this complexity using training images that incorporate typical local structures and textures, and to derive distributional properties from such training images for purposes of stochastic simulation. The large number of local pattern possibilities requires dimension reduction, then methods for pattern sampling and data conditioning towards simulation require careful attention.
520 $a This thesis proposes a method for grouping local training image patterns into classes based on a numerical scoring of these patterns that uses local filters. The pattern scoring and classification methods are developed for categorical attributes such as lithology, as well as continuous geologic attributes such as petrophysical properties, and are illustrated both for two-dimensional as well as three-dimensional structures. Stochastic pattern simulation then proceeds sequentially by retrieving conditioning data in the neighborhood of the current simulation node, identifying the pattern class most consistent with these conditioning data, and then sampling a local pattern from the identified pattern class. The sampled pattern is centered at the simulation node. This process proceeds until all nodes are simulated. The code for this pattern classification and stochastic simulation algorithm is termed filtersim .
520 $a filtersim is used to create sample simulations using a variety of training images, including an actual 3D case study. The simulation results show that filtersim appears to be practical, computationally efficient, and faithful to the structure information contained in the training image.
590 $a School code: 0212.
650 4 $a Geology. $3 516570
650 4 $a Statistics. $3 517247
650 4 $a Engineering, Petroleum. $3 1018448
690 $a 0372
690 $a 0463
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710 2 0 $a Stanford University. $3 754827
773 0 $t Dissertation Abstracts International $g 67-02B.
790 1 0 $a Switzer, Paul, $e advisor
790 $a 0212
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3209066