Stochastic analysis of nonstationary subsurface solute transport: 2. Conditional moments
Stochastic analysis of nonstationary subsurface solute transport: 2. Conditional moments
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DOI：
10.1029/wr025i011p02331
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发表时间：
1989-11
影响因子：
5.4
通讯作者：
W. Graham;D. McLaughlin
中科院分区：
文献类型：
--
作者：
W. Graham;D. McLaughlin
地下输运的随机分析表明，个别溶质羽流的浓度分布可能会显着偏离无条件合奏统计预测，特别是在近源区域。本文提出了一种改进的浓度预测，这是适合现场的具体条件的方法。改进的预测得到的条件集合矩的日志水力传导率，头和溶质浓度的现场观测。条件矩是从一个分布参数凯曼过滤器，这是递归线性化的溶质浓度和速度的最新估计。的调理过程中示出了两个合成的随机溶质羽流。合理良好的溶质浓度分布的估计，通过调节位于高浓度的不确定性区域的少量测量的系综矩。随着时间的推移，采样网络会适应每个羽流的独特特征。算例表明，尽可能早地捕捉速度场的主要趋势是很重要的。随着更多的测量变得可用，平流占小尺度速度变化的更大部分，和宏观色散项的幅度减小。这反映在条件集合矩的行为中。
Stochastic analyses of subsurface transport indicate that the concentration distributions of individual solute plumes may deviate significantly from those predicted by unconditional ensemble statistics, particularly in near-source regions. This paper presents a method for developing improved concentration predictions which are tailored to site-specific conditions. The improved predictions are obtained by conditioning ensemble moments on field observations of log hydraulic conductivity, head, and solute concentration. The conditional moments are obtained from a distributed parameter Kaiman filter which is recursively linearized about the most recent estimates of solute concentration and velocity. The conditioning procedure is illustrated for two synthetic random solute plumes. Reasonably good estimates of the solute concentration distributions are obtained by conditioning the ensemble moments on a small number of measurements located in regions of high concentration uncertainty. The sampling networks adapt to the unique characteristics of each plume as they evolve over time. The example indicates that it is important to capture the dominant trends of the velocity field at as early a time as possible. As more measurements become available, advection accounts for a greater portion of small-scale velocity variability, and the magnitude of the macrodispersion term diminishes. This is reflected in the behavior of the conditional ensemble moments.