使用贝叶斯的层次空间模型,生成美国境内土壤的地质化学和矿物学分布
Kristin J Bondo1,2, Tiffany M Wolf1,2, W David Walter3
1Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN 55108, USA.
MethodsX
|March 9, 2026
概括
我们开发了一个贝叶斯模型工具来绘制美国各地的土壤地质化学和矿物学图. 这为矿产资源,生态系统健康和环境风险评估提供了必要的数据.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 计算科学 计算科学
背景情况:
- 描述土壤的地质化学和矿物学分布对于资源管理,生态系统研究和环境风险评估至关重要.
- 美国境内的现有土壤地质化学光谱数据有限,这阻碍了全面的分析.
- 需要对土壤特性进行预测测绘,以填补数据缺口并支持各种应用.
研究的目的:
- 介绍贝叶斯建模工作流程和工具,用于生成预测地质化学和矿物学分布图的边界美国.
- 展示本工作流程的应用,用于绘制与脊椎动物健康相关的基本微量元素.
- 为各种科学应用提供了一个用于绘制39个额外元素和21种矿物质的框架.
主要方法:
- 使用集成嵌套拉普拉斯近似 (INLA) 与随机局部微分方程方法用于贝叶斯建模.
- 集成的土壤地理统计数据与环境共变量,包括土壤特性,地形,气候和土地覆盖.
- 开发了一种工作流程,以生成元素和矿物分布的连续的地理空间层 (格子).
主要成果:
- 成功生成了对美国的土壤地质化学和矿物学的预测分布.
- 模拟了关键微量元素 (Co, Cu, Fe, Mn, Se, Zn) 对于脊椎动物健康至关重要的空间分布.
- 工作流可以扩展到其他39个主要/微量元素和21种土壤矿物质的映射.
结论:
- 开发的贝叶斯建模工作流可以有效地在很大的空间范围内生成预测性土壤地质化学和矿物学地图.
- 这些地图为了解矿产资源,生态系统过程和环境风险提供了宝贵的数据.
- 该工具通过提供可访问的地理空间数据来支持广泛的生态,环境和农业应用.
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