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Updated: Jun 17, 2025

$Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples$

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

使用深度神经网络处理XRD模式的相位量化

Titouan Simonnet¹, Sylvain Grangeon², Francis Claret²

¹Institut Denis Poisson, Université d'Orléans, Université de Tours, CNRS, France.

|August 12, 2024

概括

本研究引入了一种新的神经网络 (NN),用于使用粉末X射线衍射 (XRD) 数据进行自动化矿物相识别和量化. 该方法显著加快了分析速度,特别是在大型数据集中,提高了准确性和效率.

科学领域:

材料科学材料科学材料科学
地质地质地质地质地质地
计算科学计算科学

背景情况:

准确的矿物质识别和量化对于预测材料性质至关重要.
粉末X射线衍射 (XRD) 与瑞特维尔德精细化是矿物质定量化的标准,但需要手动相位识别.
手动相位识别对于大型数据集来说是耗时且不切实际的,例如同步射线衍射计算断层扫描中的数据集.

研究的目的:

开发和验证一种新的神经网络 (NN) 方法,用于自动化从XRD数据中识别和量化矿物阶段.
克服XRD分析中手动相位识别的局限性,特别是对于大而复杂的数据集.
为任何数据集提供适用于矿物学分析的免费可用的多功能工具.

主要方法:

使用XRD模式计算代码生成大型合成XRD数据集,用于NN培训.
开发一个专门的损失函数用于比例推断,以提高NN的性能,效率和稳定性.
仅在合成数据上训练神经网络,并在合成和真实实验XRD模式上测试其性能.

主要成果:

训练有素的NN在合成和真实XRD模式中准确地识别和量化矿物阶段.
实现了较低的误差率:合成数据的阶段量化率为0.5%,实验数据的误差率为6% (石,石,多洛石,血石).
证明了NN处理对比晶体结构的能力,以及其适用于各种矿物学数据集的适用性.

结论:

拟议的NN方法在自动化矿物阶段识别和从XRD数据量化方面取得了重大进展.
这种方法使大数据集的有效分析成为可能,克服了与手工方法相关的瓶.
这种免费可用的工具在各种矿物学分析中具有广泛的适用性,无论具体的阶段是什么.

关键词:

石灰岩石的石灰岩石是一种石.复合材料是一种复合材料.计算建模计算建模深度神经网络是一个神经网络.杜洛米特是一种多洛米特.吉布斯网站吉布斯网站血的血是一种血.粉末X射线衍射粉末的X射线.

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