Hansen Scott K, Vesselinov Velimir V
Computational Earth Science Group, Earth and Environmental Sciences Division (EES-16), Los Alamos National Laboratory, Los Alamos, NM87545, United States.
J Contam Hydrol. 2016 Oct;193:74-85. doi: 10.1016/j.jconhyd.2016.09.003. Epub 2016 Sep 9.
We develop empirically-grounded error envelopes for localization of a point contamination release event in the saturated zone of a previously uncharacterized heterogeneous aquifer into which a number of plume-intercepting wells have been drilled. We assume that flow direction in the aquifer is known exactly and velocity is known to within a factor of two of our best guess from well observations prior to source identification. Other aquifer and source parameters must be estimated by interpretation of well breakthrough data via the advection-dispersion equation. We employ high performance computing to generate numerous random realizations of aquifer parameters and well locations, simulate well breakthrough data, and then employ unsupervised machine optimization techniques to estimate the most likely spatial (or space-time) location of the source. Tabulating the accuracy of these estimates from the multiple realizations, we relate the size of 90% and 95% confidence envelopes to the data quantity (number of wells) and model quality (fidelity of ADE interpretation model to actual concentrations in a heterogeneous aquifer with channelized flow). We find that for purely spatial localization of the contaminant source, increased data quantities can make up for reduced model quality. For space-time localization, we find similar qualitative behavior, but significantly degraded spatial localization reliability and less improvement from extra data collection. Since the space-time source localization problem is much more challenging, we also tried a multiple-initial-guess optimization strategy. This greatly enhanced performance, but gains from additional data collection remained limited.
我们针对一个此前未被表征的非均质含水层饱和带中的点源污染释放事件定位，开发了基于经验的误差范围。在该含水层中已钻了若干截获羽流的监测井。我们假设含水层中的水流方向是精确已知的，并且根据源识别之前的监测井观测，流速在我们最佳猜测值的两倍范围内已知。其他含水层和源参数必须通过对流 - 弥散方程对监测井突破数据的解释来估计。我们利用高性能计算生成大量含水层参数和监测井位置的随机实现，模拟监测井突破数据，然后采用无监督机器学习优化技术来估计源的最可能空间（或时空）位置。通过将多次实现中这些估计的准确性制成表格，我们将90%和95%置信范围的大小与数据量（监测井数量）和模型质量（在具有通道化水流的非均质含水层中，对流 - 弥散方程解释模型对实际浓度的保真度）相关联。我们发现，对于污染物源的纯空间定位，增加的数据量可以弥补降低的模型质量。对于时空定位，我们发现了类似的定性行为，但空间定位可靠性显著降低，额外数据收集带来的改善也较少。由于时空源定位问题更具挑战性，我们还尝试了多初始猜测优化策略。这大大提高了性能，但额外数据收集带来的收益仍然有限。
