A Machine-Learning Approach to Quantitative Phase Analysis of Whole Rocks and Sediments

研究天然岩石和沉积物的化学成分通常需要量化不同矿物和相关物质的含量。学术研究也是如此，环境咨询、石油开采和采矿公司的研究也是如此。该项目旨在提高进行这些测量的标准方法--粉末X射线衍射法的质量。为此目的使用粉末X射线衍射的主要问题是，当存在多种矿物时，分析员需要提供相当有限的可供测试的可能矿物清单。为了成功地做到这一点，分析师必须拥有一定的背景知识，了解哪些矿物是常见的，哪些是稀有的，以及哪些矿物往往一起出现在相同的地质环境中。因此，新手分析师以得出令人难以置信甚至奇怪的结果而臭名昭著。该项目将通过使用人工智能技术来解决这个问题，使选择要包括在分析中的矿物的过程更加自动化，允许新手分析师产生更可靠的结果。该项目将测试通过机器学习技术生成的计算机算法可以增强流行的粉末X射线衍射分析技术，以自动或半自动方式提供可信的定量物相分析的假设。最初的框架是一个程序(RockJockML)，它将样品衍射图案与包含约150种矿物的库中的实验图案进行匹配。将使用美国矿物学家晶体结构数据库中与RockJockML标准库中的矿物样品类似的晶体结构数据，生成大量粉末X射线衍射图和X射线荧光分析的合成数据集。合成数据集中的单个样本将被模拟为在矿物演化数据库(MED)中随机选择的地点一起发现的1-15种矿物的混合物，因此该数据集将模拟已知的矿物流行和共生模式。各种机器学习算法将被输入训练集，以产生最好地预测每个样本中包括哪些阶段的模型。然后，最好的模型将被合并到RockJockML中，该软件的版本将用MatLab和Python生成。调查人员还将制作一个数据库工具，用于向RockJockML数据库添加阶段。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Studies of the chemistry of natural rocks and sediments usually require quantification of how much of different minerals and related materials are present. This is true for academic studies, as well as those by environmental consulting, petroleum extraction, and mining companies. This project is meant to improve the quality of a standard method of making these measurements, powder X-ray diffraction. The main problem with using powder X-ray diffraction for such a purpose is that when multiple minerals are present, the analyst needs to supply a rather limited list of possible minerals for which to test. To do this successfully, the analyst must have a certain amount of background knowledge about which minerals are common or rare, and which tend to occur together in the same geologic environments. Consequently, novice analysts are notorious for producing implausible, or even strange, results. This project will address this problem by using artificial intelligence techniques to make the process of choosing minerals to include in an analysis more automated, allowing novice analysts to produce more reliable results.The project will test the hypothesis that computer algorithms generated via machine learning techniques can enhance a popular powder X-ray diffraction analysis technique to deliver credible quantitative phase analyses in an automated, or semi-automated, manner. The starting framework is a program (RockJockML) that fits sample diffraction patterns with experimental patterns in a library that includes ~150 minerals. A large synthetic data set of powder X-ray diffraction patterns and X-ray fluorescence analyses will be generated using crystal structure data from the American Mineralogist Crystal Structure Database for mineral specimens that are similar to those in RockJockML's standards library. The individual samples in the synthetic data set will be modeled as mixtures of 1-15 minerals found together at randomly chosen locations included in the Mineral Evolution Database (MED), so that the data set will mimic known mineral prevalence and co-occurrence patterns. Various machine learning algorithms will be fed the training sets to produce models that best predict which phases are included in each sample. The best models will then be incorporated into RockJockML, and versions of the software will be produced in MATLAB and Python. The investigators will also produce a database tool for adding phases to the RockJockML database.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.