评估空间启用机器学习方法,以深度到基岩绘制,加拿大阿尔伯塔省,加拿大
Steven M Pawley1, Lisa Atkinson1, Daniel J Utting1
1Alberta Geological Survey, Alberta Energy Regulator, Edmonton, Alberta, Canada.
PloS one
|March 27, 2024
概括
机器学习使用先进的空间技术准确地绘制沉积物厚度 (深度到基石) 的地图. 这种方法克服了传统方法在不同地形上的局限性,改善了地质和环境预测.
科学领域:
- 地质科学是地球科学.
- 机器学习 机器学习
- 空间分析 空间分析
背景情况:
- 深度到基岩 (DTB) 地图对于水文地质学,工程和资源管理至关重要.
- 传统的DTB估计方法在复杂,多样化的地形中难以准确.
- 准确的DTB映射对于许多实际应用是必不可少的.
研究的目的:
- 开发和评估用于预测加拿大阿尔伯塔省DTB的机器学习方法.
- 提高DTB绘图的准确性和可靠性,特别是在具有挑战性的地形中.
- 为了比较机器学习与传统空间插值方法的性能.
主要方法:
- 利用钻井石学数据来训练自然语言模型,扩大数据集.
- 采用机器学习算法 (XGBoost,随机森林,立方体) 与空间特征工程.
- 包含地理坐标,近距离测量和空间落后的DTB估计.
主要成果:
- 结合空间滞后变量的机器学习模型显著优于仅使用辅助预测器或坐标的模型.
- 提出的方法证明了可靠的DTB预测在不同的生理地理区域和省级.
- 空间滞后变量改善了预测性能,而不会产生虚假的空间工件.
结论:
- 开发的机器学习方法为DTB映射提供了可靠和广泛适用的方法.
- 这种技术提高了沉积物厚度估计在复杂地形地区的准确性.
- 这些发现对地质科学研究和需要DTB数据的应用领域有重大影响.
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