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首页> 外文期刊>Geosciences >Comparison of Two Ensemble Kalman-Based Methods for Estimating Aquifer Parameters from Virtual 2-D Hydraulic and Tracer Tomographic Tests
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Comparison of Two Ensemble Kalman-Based Methods for Estimating Aquifer Parameters from Virtual 2-D Hydraulic and Tracer Tomographic Tests

机译：从虚拟2-D液压和示踪断层测试估算含水层参数的基于组合卡尔曼的两种基于组合的基于组合方法的比较

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摘要

We compare two ensemble Kalman-based methods to estimate the hydraulic conductivity field of an aquifer from data of hydraulic and tracer tomographic experiments: (i) the Ensemble Kalman Filter (EnKF) and (ii) the Kalman Ensemble Generator (KEG). We generated synthetic drawdown and tracer data by simulating two pumping tests, each followed by a tracer test. Parameter updating with the EnKF is performed using the full transient signal. For hydraulic data, we use the standard update scheme of the EnKF with damping, whereas for concentration data, we apply a restart scheme, in which solute transport is resimulated from time zero to the next measurement time after each parameter update. In the KEG, we iteratively assimilate all observations simultaneously, here inverting steady-state heads and mean tracer arrival times. The inversion with the dampened EnKF worked well for the transient pumping-tests, but less for the tracer tests. The KEG produced similar estimates of hydraulic conductivity but at significantly lower costs. We conclude that parameter estimation in well-defined hydraulic tests can be done very efficiently by iterative ensemble Kalman methods, and ambiguity between state and parameter updates can be completely avoided by assimilating temporal moments of concentration data rather than the time series themselves.
机译：我们比较了基于合奏的基于卡尔曼的方法来估计液压和示踪断层扫描实验数据的含水液的液压导电场：（i）合奏卡尔曼滤波器（ENKF）和（ii）Kalman集合发电机（Keg）。我们通过模拟两个泵送测试来生成合成绘制和跟踪数据，每个泵测试随后是一个示踪剂测试。使用全瞬态信号执行与ENKF的参数更新。对于液压数据，我们使用enkf的标准更新方案随着阻尼，而对于浓度数据，我们应用重启方案，其中在每个参数更新后从时间零点重新刻上螺旋传输到下一个测量时间。在柯格中，我们同时迭代同时吸收所有观察，在这里反转稳态头和平均示踪到达时间。抑制ENKF的反转效果良好，对于瞬态泵送测试，但对于示踪剂测试较少。 KEG生产了类似的液压导电性估计，但成本明显降低。我们得出结论，通过迭代集合Kalman方法可以非常有效地完成明确定义的液压试验中的参数估计，并且可以通过吸收浓度数据的时间矩而不是时间序列本身来完全避免状态和参数更新之间的模糊性。

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