A Three⁃Dimensional Geological Modeling and Uncertainty Analysis Framework Integrating Semi⁃Supervised Deep Learning and Multiple⁃Point Geostatistics

Zhou Shunping; Yuan Chuang; Chu Deping; Fang Fang; Pan Shengyong; Liu Zhaoxin; Li Hong; Wan Bo

doi:10.3799/dqkx.2026.093

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A Three⁃Dimensional Geological Modeling and Uncertainty Analysis Framework Integrating Semi⁃Supervised Deep Learning and Multiple⁃Point Geostatistics

更新时间：2026-10-15

- A Three⁃Dimensional Geological Modeling and Uncertainty Analysis Framework Integrating Semi⁃Supervised Deep Learning and Multiple⁃Point Geostatistics
- Earth Science Vol. 51, Issue 9, Pages: 3547-3561(2026)
- 作者机构：
  
  1.中国地质大学（武汉）计算机学院，湖北武汉 430078
  2.中国地质大学（武汉）地理与信息工程学院，湖北武汉 430078
  3.武汉中地数码科技有限公司，湖北武汉 430074
  4.四川省综合地质调查研究所，四川成都 610081
  5.四川省第五地质大队，四川成都 610081
- 作者简介：
- 基金信息：
- DOI：10.3799/dqkx.2026.093
  CLC： P628
- Received：22 December 2025，
  
  Published：30 September 2026
- 稿件说明：
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周顺平，袁创，储德平，方芳，潘声勇，刘兆鑫，李红，万波，2026.一种融合半监督深度学习与多点地质统计的三维地质建模及不确定性评估框架.地球科学，51（9）：3547-3561.

Zhou Shunping，Yuan Chuang，Chu Deping，Fang Fang，Pan Shengyong，Liu Zhaoxin，Li Hong，Wan Bo，2026.A Three⁃Dimensional Geological Modeling and Uncertainty Analysis Framework Integrating Semi⁃Supervised Deep Learning and Multiple⁃Point Geostatistics.Earth Science，51（9）：3547-3561.
周顺平，袁创，储德平，方芳，潘声勇，刘兆鑫，李红，万波，2026.一种融合半监督深度学习与多点地质统计的三维地质建模及不确定性评估框架.地球科学，51（9）：3547-3561. DOI： 10.3799/dqkx.2026.093.

Zhou Shunping，Yuan Chuang，Chu Deping，Fang Fang，Pan Shengyong，Liu Zhaoxin，Li Hong，Wan Bo，2026.A Three⁃Dimensional Geological Modeling and Uncertainty Analysis Framework Integrating Semi⁃Supervised Deep Learning and Multiple⁃Point Geostatistics.Earth Science，51（9）：3547-3561. DOI： 10.3799/dqkx.2026.093.

摘要

针对钻孔数据稀疏、类别不均衡及复杂地质结构导致的三维地质建模精度低、空间连续性差和不确定性高的问题，提出一种融合半监督深度学习与多点地质统计的分阶段联合建模方法.首先，构建基于邻域空间上下文特征的半监督空间感知注意力网络（SAAN），利用伪标签机制提升地层分类精度并生成初始模型；随后，设计多点地质统计随机邻域搜索（RNS）策略对低置信度与地质语义冲突区域进行迭代优化；最后，提出协同一致性熵量化两阶段预测一致性及联合不确定性.以成都平原地区钻孔数据为例，所提方法在地层空间分布再现、边界刻画及结构连续性方面优于反距离加权（IDW）、支持向量机（SVM）、随机森林（RF）、多层感知机（MLP）及Kolmogorov-Arnold网络（KAN）等基线方法.此外，协同一致性熵能有效识别结构性不确定性，避免高熵值但预测一致的误判.所提出的分阶段联合建模框架能在有限、不均衡钻孔数据条件下实现高精度、结构合理且不确定性可控的三维地质模型重建，为复杂地质环境下的地下结构刻画与工程应用提供有效技术支撑.

Abstract

To address the problems of low accuracy

poor spatial continuity and high uncertainty in 3D geological modeling caused by sparse borehole data

unbalanced categories and complex geological structures

a phased joint modeling method integrating semi-supervised deep learning and multiple-point geostatistics is proposed in this paper. Firstly

a semi-supervised Spatial-Aware Attention Net (SAAN) based on neighborhood spatial context features is constructed

and the pseudo-label mechanism is utilized to improve the classification accuracy of strata and to generate the initial model; Subsequently

the multiple-point geostatistics Random Neighborhood Search (RNS) strategy is combined to iteratively optimize the areas with low confidence and geological semantic conflicts; Finally

the collaborative consistency entropy is proposed to quantify the two-stage prediction consistency and joint uncertainty. Taking the borehole data in the Chengdu Plain area as an example

the proposed method outperforms baseline methods such as Inverse Distance Weighting (IDW)

Support Vector Machine (SVM)

Random Forest (RF)

Multi-layer Perceptron (MLP)

and Kolmogorov-Arnold Networks (KAN) in terms of reproduction of stratum spatial distribution

boundary characterization

and structural continuity. In addition

the collaborative consistency entropy can effectively identify structural uncertainties and avoid misjudgments of high entropy values but consistent predictions. The proposed phased joint modeling framework can achieve high-precision

structurally reasonable and uncertainty controllable three-dimensional geological model reconstruction under the conditions of limited and unbalanced borehole data

providing effective technical support for the characterization and engineering application of underground structures in complex geological environments.

关键词

Keywords

references

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