Posterior assessment of parameters in a time domain random walk model of partitioning tracer tests in two-phase flow scenarios

Supplementary material associated with the manuscript : "Posterior assessment of parameters in a time domain random walk model of partitioning tracer tests in two-phase flow scenarios" by Emanuela Bianchi Janetti, Alberto Guadagnini and Monica Riva.
The folder '1. Input Data' contains the set of experimental observations of Dwarakanath et al. (1999) analysed in the manuscript.
The folder '2. Classical ML inversion' inlcudes the breakthrough curves obtained with the Time Domain Random Walk (TDRW) particle tracking methodology considering the parameters estimated via classical Maximum Likelihood (ML) approach.
The folder '3. Stochastic inversions' inlcudes the key results of the stochastic inverse modeling technique.

与论文《时域随机游走模型在两相流场景中分割示踪剂试验参数后验评估》相关联的补充材料：Emanuela Bianchi Janetti、Alberto Guadagnini 和 Monica Riva 合著。文件夹“1. 输入数据”包含了论文中分析的 Dwarakanath 等人（1999 年）的实验观测数据集。文件夹“2. 经典机器学习反演”中包含了采用时域随机游走（TDRW）粒子追踪方法，并考虑通过经典最大似然（ML）方法估计的参数得到的突破曲线。文件夹“3. 随机反演”中包含了随机逆建模技术的关键结果。