.png){kind=icon}
返回
Model selection in fracture mapping from elastostatic data
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijsolstr.2006.06.022.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, the inverse problem of imaging internally pressurized cracks from elastostatic measurements is investigated with special attention to the question of model choice. The selection of the most probable model from among a finite set of fracture geometry and loading model is carried out using Bayes factors. The modelling error variance is also estimated during the inversion procedure. This Bayesian model selection method also produces a known limit for the resolution of fracture dimensions, which depends on the configuration of the measurements. Both synthetic and real field examples in hydraulic fracture mapping applications are presented. Crown copyright (c) 2006 Published by Elsevier Ltd. All rights reserved.
Keyword:
crack detection
inverse problem
Bayesian model selection
hydraulic fracturing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
1.2W
被引数:
3.1W
机构
暂无机构信息
引用论文
.png){kind=icon}
Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks
PREAI
分享收藏Identification of cracks and cavities using the topological sensitivity boundary integral equation
PREAI
分享收藏分享收藏
查看更多内容