| 岩土力学参数空间变异性的集合卡尔曼滤波佶值 |
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| 引用本文： | 赵红亮,冯夏庭,张东晓,周辉.岩土力学参数空间变异性的集合卡尔曼滤波佶值[J].岩土力学,2007,28(10):2219-2223,2228. |
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| 作者姓名： | 赵红亮 冯夏庭 张东晓 周辉 |
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| 作者单位： | [1]中国科学院武汉岩土力学研究所岩土力学与工程国家重点实验室,武汉430071 [2]俄克拉荷马大学地球与能源学院石油与地质工程系,美国73019 |
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| 基金项目： | 国家自然科学基金国际（地区）合作与交流项目（No.50340420444）;中国科学院海外杰出青年基金（No.2005-1-1）. |
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| 摘 要： | 岩土参数具有结构性和随机性的空间变异特征，该特征导致岩土参数具有不确定性。以地质统计学作为岩土参数空间变异性分析的理论基础，将分布于研究区的岩土参数视为区域化变量，变异函数既描述了岩土参数整体的空间结构性变化，又描述了其局部的随机性变化，用变异函数理论模型作为描述岩土参数空间变异规律的数学模型。引入集合卡尔曼滤波（EnKF）分析方法，利用时空分布的观测数据，对岩土参数空间变异性进行估值。数值算例表明，EnKF能够有效地融合观测数据，较好地提供岩土参数空间变异性的估值。
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| 关 键 词： | 空间变异性 地质统计学 不确定性 岩土力学参数 集合卡尔曼滤波 |
| 文章编号： | 1000-7598-（2007）10-2219-06 |
| 修稿时间： | 2006-09-11 |
Spatial variability of geomechanical parameter estimation via ensemble kalman filter |
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| ZHAO Hong-liang, FENG Xia-ting, ZHANG Dong-xiao, ZHOU Hui.Spatial variability of geomechanical parameter estimation via ensemble kalman filter[J].Rock and Soil Mechanics,2007,28(10):2219-2223,2228. |
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| Authors: | ZHAO Hong-liang FENG Xia-ting ZHANG Dong-xiao ZHOU Hui |
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| Institution: | l. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; 2. Mewbourne School of Petroleum and Geological Engineering, the University of Oklahoma, Norman, OK 73019, USA |
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| Abstract: | Geomechanical parameters have structural and stochastic properties on spatial variability, which causes the uncertainty of geomechanical parameters. Geostatistics is used as the theoretical foundation for analyzing the spatial variability of geomechanical parameters; and geomechanical parameters distributing in region of interest are considered as zonal variables. Variogram function can not only describe the integral spatial structural variety, but also describe the local stochastic variety. Therefore, theoretical mode/of variogram function is employed as the mathematical model for depicting the spatial variability law of geomechanical parameters. The ensemble Kalman filter method is introduced to estimate the spatial variability of geomechanical parameters using the observation data with temporal and spatial variation. Demonstrated by numerical example, the EnKF can effectively incorporate the observation data and successfully provide the spatial variability estimation of geomechanical parameters. |
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| Keywords: | spatial variability geostatistics uncertainty geomechanical parameter ensemble Kalman filter |
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