使用XGBoost预测地热热流与合煤床甲和地热资源的应用
Hongyang Zhang1,2, Kewen Li1,2, Shuaihang Shi1,2
1School of Energy Resources, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China.
ACS omega
|June 16, 2025
概括
一个XGB模型使用地质数据预测地热热流 (GHF),改进了煤床甲 (CBM) 勘探. 该模型准确地绘制了热条件图,揭示了煤炭丰富地区更高的GHF.
科学领域:
- 地质物理学 地质物理学
- 地质化学 地质化学
- 机器学习 机器学习
背景情况:
- 储温度对煤炭,煤床甲 (CBM) 和地热热流 (GHF) 的形成和生产产生重大影响.
- 温室气体是地下热条件的关键指标,对能源资源评估至关重要.
研究的目的:
- 开发和评估一种极端梯度增强 (XGB) 模型,用于预测中国的GHF.
- 评估地质和地质物理特征对GHF的影响及其与CBM潜力的关系.
主要方法:
- 在全球和中国特定数据集上使用12个地质/地物理特征训练XGB模型.
- 使用MAE,RMSE和R2评估模型性能;使用SHAP值分析特征重要性.
- 预测的热流图被生成并与 kriging-interpolated 图进行比较,以获得一致性和地质连贯性.
主要成果:
- 与全球模型 (R2 = 0.65) 相比,中国的XGB模型在低热流区域 (R2 = 0.73) 显示出更高的性能.
- 生成的热流图显示了更平稳的空间趋势和与测量数据的更强的相关性.
- 在中高级煤炭地区观察到明显更高的GHF,这表明CBM潜力增强.
结论:
- XGB模型准确地预测了GHF,为地热资源勘探和CBM开发提供了宝贵的见解.
- 地结构和火山活动是GHF的主要控制因素,指导未来的勘探工作.
- 该模型的预测能力在能源资源评估的数据稀缺地区尤其有利.
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