Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter-to-observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model-to-measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal-to-noise ratio related to observations influential to the parameter estimation even as the number of forward runs decrease. In this work a simultaneous increments, an iterative ensemble smoother (IES), and a randomized Jacobian approach were compared to a traditional approach that uses a full Jacobian matrix. All approaches were applied to the same model developed for decision making in the Mississippi Alluvial Plain, USA. Both the IES and randomized Jacobian approach achieved a desirable fit and similar parameter fields in many fewer forward runs than the traditional approach; in both cases the fit was obtained in fewer runs than the number of adjustable parameters. The simultaneous increments approach did not perform as well as the other methods due to inability to overcome suboptimal dropping of parameter sensitivities. This work indicates that use of highly efficient algorithms can greatly speed parameter estimation, which in turn increases calibration vetting and utility of realistic models used for decision making.

中文翻译：

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评估用于校准大型环境模型的较低计算负担的方法

用于决策的现实环境模型通常需要高度参数化的方法。这种模型的校准是计算密集型的，因为广泛使用的参数估计方法需要对每个调整的参数进行单独的正向运行。这些运行构建了用于开发候选参数升级的参数对观测敏感度或雅可比矩阵。参数估计算法通常还受到雅可比矩阵内计算的灵敏度中的数值噪声的不利影响，这可能导致不必要的参数估计迭代和较少的模型与测量拟合。理想情况下，减少参数估计计算负担的方法也将增加与影响参数估计的观察相关的信噪比，即使向前运行的次数减少。在这项工作中，将同时增量、迭代集成平滑器 (IES) 和随机雅可比方法与使用完整雅可比矩阵的传统方法进行了比较。所有方法都应用于为美国密西西比冲积平原的决策制定的同一模型。与传统方法相比，IES 和随机雅可比方法都在更少的前向运行中实现了理想的拟合和相似的参数字段；在这两种情况下，拟合的运行次数都少于可调整参数的数量。由于无法克服参数敏感性的次优下降，同步增量方法的性能不如其他方法。这项工作表明，使用高效算法可以大大加快参数估计的速度，从而增加校准审查和用于决策的现实模型的实用性。