基于ArcGIS空间插值的干热岩温度预测研究
饶俊¹，罗兵²，丁海萍¹，李中隆¹
（1.江西交通职业技术学院，江西 南昌 330013；2.江西核工业测绘院集团有限公司，江西 南昌 330038）
摘要： 克里格空间局部插值方法是地统计学的重要内容之一，根据不同研究对象的空间分布规律选定适合的变异函
数。干热岩的空间数据分布既有随机性又有结构性，影响干热岩温度的因素有地质构造、放射性物质组成、地层活动等。
研究利用克里格插值法估算地下空间无法测量的温度。实验结果表明，变异函数选取高斯模型的插值效果最好，而克里
格插值法效果好于反距离权重法。在数据量有限的情况下，克里格插值法能有效计算出地层深部温度的分布情况。同时
变异函数对插值计算的最终结果起到了决定性作用，实验样本点的选取也直接影响到插值计算的精度。
关键词： 地统计学；克里格；变异函数；干热岩
中图分类号： P314      文献标志码： A      文章编号： 2095-0802-(2023)06-0014-03
Prediction of Hot Dry Rock Temperature Based on ArcGIS
Spatial Interpolation
RAO Jun¹, LUO Bing², DING Haiping¹, LI Zhonglong¹
(1. Jiangxi Vocational and Technical College of Communications, Nanchang 330013, Jiangxi, China;
2. Jiangxi Nuclear Industry Surveying and Mapping Institute Group Co., Ltd., Nanchang 330038, Jiangxi, China)
Abstract: Kriging spatial local interpolation method is one of the important contents of geostatistics, which selects suitable
variation functions according to the spatial distribution rules of different research objects. The spatial data distribution of hot dry
rocks has both randomness and structure, and the factors that affect the temperature of hot dry rocks include geological structure,
radioactive material composition, and stratigraphic activity. In the study, Kriging interpolation method was used to estimate the
temperature of underground space that cannot be measured. The experimental results show that the Gaussian model selected by
the variation function has the best interpolation effect, and the Kriging interpolation effect is better than the inverse distance
weighting method. In the case of limited data, Kriging interpolation method can effectively calculate the distribution of temperature
in the deep stratum. At the same time, the variation function plays a decisive role in the final result of interpolation calculation,
and the selection of experimental sample points also directly affects the accuracy of interpolation calculation.

Key words: geostatistics; Kriging; variation function; hot dry rock