Calcium carbonate (CaCO₃), a mineral commonly found in the Earth's crust, is mainly in the forms of calcite and aragonite. Calcite has the most stable type of thermodynamics at room temperature and ambient pressure. It has wide band gap structure and is of great interest for a wide-range technical and industrial applications due to its physical properties and suitability. In this study, a new method based on the long short-term memory (LSTM) model of deep learning is proposed to classify the thermoluminescence properties such as fading, cycle of measurement, heating rate, and dose–response of CaCO₃. Therefore the thermoluminescence properties of calcite was investigated as a suitable band structure and its coherent data were used to classify the features using a deep learning LSTM model. Experiments were carried out on a dataset consisting of four classes. The accuracy, precision, and sensitivity values of the proposed model obtained were 98.34, 97.90, and 98.56%, respectively. The classification process of the results obtained from the designed model showed good performance.

中文翻译：

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长短期记忆模型对CaCO3热释光特征的分类

碳酸钙 (CaCO ₃ ) 是一种常见于地壳中的矿物，主要以方解石和文石的形式存在。方解石在室温和环境压力下具有最稳定的热力学类型。它具有宽带隙结构，由于其物理特性和适用性，在广泛的技术和工业应用中具有重要意义。在这项研究中，提出了一种基于深度学习的长短期记忆 (LSTM) 模型的新方法来对 CaCO ₃的热释光特性进行分类，例如衰落、测量周期、加热速率和剂量响应. 因此，方解石的热释光特性被研究为合适的能带结构，其相干数据用于使用深度学习 LSTM 模型对特征进行分类。实验是在由四个类组成的数据集上进行的。所得模型的准确度、精密度和灵敏度值分别为 98.34、97.90 和 98.56%。从设计模型获得的结果的分类过程显示出良好的性能。