Conditioning multiple-point statistics simulations to block data

详细信息    查看全文

• 作者：Julien Straubhaar^a ; ^{julien.straubhaar@unine.ch" class="auth_mail" title="E-mail the corresponding author} ; Philippe Renard^a ; ^{philippe.renard@unine.ch" class="auth_mail" title="E-mail the corresponding author} ; Gré ; goire Mariethoz^b ; ^{gregoire.mariethoz@unil.ch" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Geostatistical simulation ; Multiple-point statistics ; Block data ; Downscaling
• 刊名：Spatial Statistics
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：16
• 期：Complete
• 页码：53-71
• 全文大小：5369 K

文摘

Multiple-points statistics (MPS) allows to generate random fields reproducing spatial statistics derived from a training image. MPS methods consist in borrowing patterns from the training set. Therefore, the simulation domain is assumed to be at the same resolution as the conceptual model, although geometrical deformations can be handled by such techniques. Whereas punctual conditioning data corresponding to the scale of the grid node can be easily integrated, accounting for data available at larger scales is challenging. In this paper, we propose an extension of MPS able to deal with block data, i.e. target mean values over subsets of the simulation domain. Our extension is based on the direct sampling algorithm and consists to add a criterion for the acceptance of the candidate node scanned in the training image to constrain the simulation to block data. Likelihood ratios are used to compare the averages of the simulated variable taken on the informed nodes in the blocks and the target mean values. Moreover, the block data may overlap and their support can be of any shape and size. Illustrative examples show the potential of the presented algorithm for practical applications.