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.

本数据集为Emanuela Bianchi Janetti、Alberto Guadagnini与Monica Riva撰写的手稿《两相流场景下分配示踪试验的时域随机游走模型参数后验评估》的配套补充材料。
文件夹‘1. Input Data’收录了本手稿中分析的Dwarakanath等人（1999）的实验观测数据集。
文件夹‘2. Classical ML inversion’包含了采用经典最大似然（Maximum Likelihood, ML）方法估计参数后，通过时域随机游走（Time Domain Random Walk, TDRW）粒子追踪方法得到的穿透曲线。
文件夹‘3. Stochastic inversions’收录了随机反演建模技术的核心研究成果。