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Using sequential self-calibration method to estimate a correlation length of a log-conductivity field conditioned upon a tracer test and limited measured data

机译:使用顺序自校准方法来估算以示踪剂测试和有限的测量数据为条件的对数电导率场的相关长度

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摘要

A gradient-based inverse method, the sequential self-calibrated method (SSC), has been developed to identify a parameter for the statistical distribution function of a conductivity field, a correlation length. The identification is based on a tracer test data and some conductivity measurements. Correlation length is an important parameter for geostatistical description of a conductivity distribution. It is generally difficult to obtain from limited field measurements, especially in the horizontal direction, because the measurement in this direction is generally limited and sparsely populated. When the SSC method is used to estimate conductivity statistical distribution conditioned upon tracer test data, the closer the chosen correlation length to the real value, the faster the convergence rate, which is the basis of the identification method proposed in this study. The study results indicate the correlation length can be well determined by the tracer data and some conductivity measurements. In comparison with the identification of correlation length with only conductivity measurement, with tracer test data, much less measurement is required.
机译:已经开发出基于梯度的逆方法,即顺序自校准方法(SSC),以识别电导率场的统计分布函数的参数,相关长度。标识基于示踪剂测试数据和一些电导率测量值。相关长度是电导率分布的地统计学描述的重要参数。通常难以从有限的现场测量中获得,尤其是在水平方向上,因为在该方向上的测量通常是有限的并且人烟稀少。当使用SSC方法估算以示踪剂测试数据为条件的电导率统计分布时,所选的相关长度越接近真实值,收敛速度越快,这是本研究提出的识别方法的基础。研究结果表明相关长度可以通过示踪数据和一些电导率测量很好地确定。与仅通过电导率测量来识别相关长度以及使用示踪剂测试数据进行识别相比,所需的测量量要少得多。

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