Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data. This study demonstrates the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations. We trained MTL models to identify phase information in {\mu}-XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models showed superior accuracy compared to binary classification CNNs. Additionally, introducing a tailored cross-entropy loss function improved MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieved close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor-intensive experimental datasets.

翻译：传统上，对热液流体环境中高度畸变的微X射线衍射（{\mu}-XRD）图谱进行分析需耗费大量时间，通常需要繁复的数据预处理和有标注的实验数据。本研究展示了采用多任务学习（MTL）架构的深度学习在克服这些局限方面的潜力。我们训练MTL模型以识别{\mu}-XRD图谱中的物相信息，从而最大程度减少对有标注实验数据的需求，并隐去预处理步骤。值得注意的是，MTL模型相较于二分类CNN展现出更高的准确率。此外，引入定制化交叉熵损失函数进一步提升了MTL模型的性能。最重要的是，经调优用于分析原始及未掩膜XRD图谱的MTL模型，其性能与处理预训练数据的模型相近，精度差异极小。本研究表明，诸如MTL等先进深度学习架构能够自动化处理繁琐的数据处理任务，简化畸变XRD图谱的分析流程，并降低对劳动密集型实验数据集的依赖。