• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Water resources research >Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography
【24h】

Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography

机译：集成卡尔曼滤波器的变换数据参数估计：液压层析成像的方法和应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Ensemble Kalman filters (EnKFs) are a successful tool for estimating state variables in atmospheric and oceanic sciences. Recent research has prepared the EnKF for parameter estimation in groundwater applications. EnKFs are optimal in the sense of Bayesian updating only if all involved variables are multivariate Gaussian. Subsurface flow and transport state variables, however, generally do not show Gaussian dependence on hydraulic log conductivity and among each other, even if log conductivity is multi-Gaussian. To improve EnKFs in this context, we apply nonlinear, monotonic transformations to the observed states, rendering them Gaussian (Gaussian anamorphosis, GA). Similar ideas have recently been presented by Beal et al. (2010) in the context of state estimation. Our work transfers and adapts this methodology to parameter estimation. Additionally, we address the treatment of measurement errors in the transformation and provide several multivariate analysis tools to evaluate the expected usefulness of GA beforehand. For illustration, we present a first-time application of an EnKF to parameter estimation from 3-D hydraulic tomography in multi-Gaussian log conductivity fields. Results show that (1) GA achieves an implicit pseudolinearization of drawdown data as a function of log conductivity and (2) this makes both parameter identification and prediction of flow and transport more accurate. Combining EnKFs with GA yields a computationally efficient tool for nonlinear inversion of data with improved accuracy. This is an attractive benefit, given that linearization-free methods such as particle filters are computationally extremely demanding.
机译：集合卡尔曼滤波器（EnKFs）是一种成功的工具，用于估算大气和海洋科学中的状态变量。最近的研究已经准备了EnKF用于地下水应用中的参数估计。仅当所有涉及的变量均为多元高斯时，EnKF才在贝叶斯更新意义上是最佳的。但是，即使测井电导率是多高斯的，地下流量和输运状态变量通常也不显示高斯对水力测井电导率的依赖性。为了在这种情况下改善EnKF，我们对观察到的状态应用了非线性的单调变换，使它们成为高斯（Gaussian anamorphosis，GA）。 Beal等人最近提出了类似的想法。 （2010）在状态估计的背景下。我们的工作将这种方法转移并调整为参数估计。此外，我们解决了转换中测量误差的处理，并提供了多个多元分析工具来预先评估GA的预期效用。为了说明，我们介绍了EnKF在多高斯对数电导率场中3D液压层析成像参数估计中的首次应用。结果表明：（1）GA实现了对数下降率数据的隐式伪线性化，它是对数电导率的函数；（2）这使得参数识别以及流量和传输的预测更加准确。将EnKF与GA结合使用可提供一种计算效率高的工具，可提高数据的非线性反演精度。鉴于无线性化方法（例如粒子滤波器）对计算的要求很高，因此这是一个诱人的好处。

著录项

  • 来源
    《Water resources research》 |2012年第4期|p.W04502.1-W04502.18|共18页
  • 作者

    A. Schonieer; W. Nowak; H.-J. Hendricks Franssen;

  • 作者单位

    Institute for Modelling Hydraulic and Environmental Systems, LH2/SimTech, Universitat Stuttgart, Pfaffenwaldring 61, D-70569 Stuttgart, Germany,BoSS Consult GmbH, Lotterbergstrasse 16, D-70499 Stuttgart, Germany;

    Institute for Modelling Hydraulic and Environmental Systems, LH2/SimTech, Universitat Stuttgart, Pfaffenwaldring 61, D-70569 Stuttgart, Germany;

    Institute of Chemistry and Dynamics of the Geosphere, ICG-4, Research Centre Juelich, Wilhelm-Johnen-Strasse,D-52425 Juelich, Germany;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号