研究因子分析和使用机器学习评估接效应的方法
Wenxin Li1, Juntao Chen2, Jun Zhu1
1Shandong Univ Sci & Technol, Coll Energy & Min Engn, Qingdao, People's Republic of China.
Scientific reports
|April 2, 2024
概括
这项研究开发了一种机器学习模型,以评估石灰岩含水层中定向接的有效性. 优化的Adaboost模型准确地评估了接效应,优于传统方法.
科学领域:
- 地质技术工程 地质技术工程
- 水文地质学 水文地质学
- 机器学习应用 机器学习应用
背景情况:
- 接地工程有效性评估至关重要但复杂.
- 传统方法通常依赖于经验数据,限制精度.
- 定向钻探和接用于石灰岩水库改造.
研究的目的:
- 分析影响石灰岩含水层接效应的因素.
- 开发和验证一个机器学习模型来评估定向接的有效性.
- 将机器学习模型的性能与传统评估方法进行比较.
主要方法:
- 建立了一个"双流程,多参数和多因素"的分析系统.
- 采用相关性分析来验证选定的工程指数.
- 利用了8个机器学习模型和3个优化算法,包括Adaboost和遗传算法.
主要成果:
- 开发的系统阐明了影响因素和工程参数之间的关系.
- 八个选定的指标的相关性很低,这证明了它们作为独立评估指标的有效性.
- 通过遗传算法优化的Adaboost模型在评估接效应方面取得了卓越的准确性.
结论:
- "双工艺,多参数和多因素"系统有效地模拟了接效应的复杂性.
- 机器学习,特别是优化的Adaboost模型,提供了更精确,更现实的定向接效率评估.
- 这些发现为改善接工程实践提供了强大的框架.
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