基于融合模型的不同酸化度的砂岩中红外光谱特征的分析
Lu Chen1,2, Longfei Chang3, Huiqing Lian4,5
1School of Emergency Technology and Management, North China Institute of Science and Technology, Beijing, 065201, China.
Scientific reports
|July 2, 2025
概括
一个新的聚变模型使用光谱分析准确地预测红色砂岩酸化. 这种方法提供了95%的准确性,非破坏性的方法来监测岩石工程对酸性地下水的稳定性.
科学领域:
- 地质技术工程 地质技术工程
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
背景情况:
- 酸性地下水溶解威胁着地质技术结构.
- 准确评估岩石酸化对于稳定性至关重要.
- 频谱分析提供了一个潜在的非破坏性监测方法.
研究的目的:
- 开发一个强大的光谱预测模型来评估红色砂岩酸化.
- 为了比较K-近邻 (KNN),支持向量机 (SVM) 和随机森林 (RF) 算法的性能.
- 建立一个融合模型,整合多个算法,以提高预测准确度.
主要方法:
- 红色砂岩样本被暴露在不同的盐酸度和持续时间.
- 里埃变换中红外光谱分析了光谱特征.
- 数据预处理包括Savitzky-Golay平滑,规范化和主要组件分析 (PCA).
- 机器学习模型 (KNN,SVM,RF) 被训练,并开发了一个融合模型.
主要成果:
- 增加的酸度和浸泡时间导致红色砂岩的反射光谱强度下降.
- 聚变模型在检测酸化度方面达到95%的准确性.
- 融合模型的性能优于单个KNN,SVM和RF模型.
结论:
- 光谱特征是红色砂岩酸化的敏感指标.
- 开发的融合模型为评估岩石酸化提供了一个高度准确的,非破坏性的方法.
- 这种方法为实时监测受酸性地下水影响的岩石工程稳定性提供了有价值的见解.
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