Deep Learning Empowers Deep Geothermal Resources Exploration: Progress and Trend
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摘要:
全球深层地热资源勘探开发正处于从“实验性探索”向“规模化应用”转型的关键阶段.以深度学习为代表的人工智能已经在大数据分析、模式识别和非线性问题求解方面展现出变革性潜力，为破解当前制约我国深层地热资源高效精准勘探的核心难题提供了新途径.推动深度学习与传统地热勘探流程的深度融合，对我国在全球深层地热资源开发利用领域提升竞争力具有重要意义.本文聚焦深度学习数据处理、建模及预测技术与深层地热资源勘探（涵盖地热地质勘查、地球物理勘探、地球化学勘探等环节）的深度结合，系统梳理并总结了地热资源勘探技术、深度学习技术方法、深度学习赋能深层地热资源勘探的关键技术方法、核心进展与研究成果，展现了深度学习赋能地热资源勘探方法相较于传统方法带来的效率、准确率与精度提升.本文最后结合前沿技术阐述了深度学习赋能深层地热资源勘探领域面临的核心挑战，未来深层地热资源智能勘探亟须聚焦多模态数据融合、可解释与可信人工智能、地热垂直领域智能计算基座与大模型建设等方面，最终实现从“经验驱动”到“知识驱动”再到“智能驱动”的跨越，为地热能源资源行业数字化智能化发展提供核心技术支撑.
Abstract:The global exploration and development of deep geothermal resources is at a critical stage, transitioning from experiment to application. Artificial intelligence, particularly deep learning, has demonstrated transformative potential in big data analysis, pattern recognition, and nonlinear problem solution, offering new pathways to address challenges hindering efficient and precise exploration of deep geothermal resources. It is significantly important to promote the integration of deep learning with traditional geothermal exploration processes to enhance China's competitiveness in the development and utilization of deep geothermal resources. This paper focuses on the integration of deep learning data processing, modeling, and prediction with deep geothermal resource exploration (including geothermal geological surveys, geophysical exploration, and geochemical exploration, etc.). It systematically reviews and summarizes key technological methods in geothermal resource exploration, deep learning techniques, and the critical advancements and research outcomes that empower deep geothermal exploration. This study demonstrates the improvement of efficiency, accuracy, and precision brought by deep-learning-based geothermal resources exploration methods compared to traditional methods. Finally, the paper discusses the core challenges with cutting-edge technologies faced by deep geothermal exploration. In future, intelligent deep geothermal resources exploration urgently needs to focus on multiple modal data fusion, interpretable and trustworthy artificial intelligence, and construction of intelligent computing foundations and large models, which ultimately, will enable a leap from "experience-driven" to "knowledge-driven" and then to "intelligent-driven", providing core technological support for the digital and intelligent development of the geothermal energy resources industry.
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图 1 地热资源勘探流程
图件来自van der Meer et al.（2014）、刘庆等（2022）、Huang et al.（2023）、Sun et al.（2023）
Fig. 1. Geothermal resource exploration process
图 3 基于监督学习的地热资源分布预测工作流程(修改自蒋恕等, 2020)
Fig. 3. Supervised-learning-based geothermal resources potential prediction (modified from Jiang et al., 2020)
图 4 基于无监督自组织映射神经网络的丹麦Triassic-Jurassic深层地热砂岩储层地震岩性识别与特征分析
图修改自Chopra et al.（2021）. a. 无监督自组织映射神经网络；b. 研究区热储岩性分类
Fig. 4. Seismic characterization of a Triassic-Jurassic deep geothermal sandstone reservoir, onshore Denmark, based on unsupervised self-organizing map neural network
图 5 基于半监督学习的孔隙度预测流程（修改自Chen et al., 2025b）
Fig. 5. Porosity prediction process based on semi-supervised learning (modified from Chen et al., 2025b)
图 6 地热勘探场景下智能体强化学习流程
Fig. 6. Reinforcement learning process of agent in geothermal exploration scenarios
图 7 基于深度学习的重力反演揭示共和盆地地热系统深部热源
图修改自Zhou et al.（2024）. a. U-Net神经网络架构；b. 研究区重力反演结果
Fig. 7. Deep learning-based gravity inversion reveals the deep heat source of the geothermal system in the Gonghe basin
图 8 基于物理信息神经网络的地热物性参数三维模拟(修改自Ishitsuka et al., 2025)
Fig. 8. 3-D modeling of geothermal physical parameters based on physics-informed neural network (modified from Ishitsuka et al., 2025)
图 9 前郭县地热资源潜力评价
a. 姚家组储层；b. 青山口组储层；c. 泉头组储层，据Zhang et al.（2023）
Fig. 9. Geothermal resource potential assessment in Qianguo County
图 10 基于CNN-LSTM融合网络的溢流早期预测
图修改自付加胜等（2021）. a. 溢流预测结果；b. 钻井参数图
Fig. 10. CNN-LSTM fusion network for early prediction of overflow
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