经修订的地热资源潜力评估证据权重模型:中国川西高原案例研究

IF 2.9 2区 地球科学 Q3 ENERGY & FUELS Geothermal Energy Pub Date : 2024-06-06 DOI:10.1186/s40517-024-00298-2
Ronghua Huang, Chao Zhang, Guangzheng Jiang, Haozhu Zhang
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引用次数: 0

摘要

有效勘探地热资源是开发和利用地热资源的基础。近年来,地理信息系统(GIS)因其强大的整合和分析与地热资源形成相关的地质、地球物理、地球化学等多源数据的能力,在勘探中得到了越来越广泛的应用。正确认识证据因素对地热资源的控制作用是证据权重模型预测结果是否准确的前提和基础。传统的证据权重模型假定各证据因素对地热资源的形成和分布具有统一的控制作用。然而,最新研究表明,受活动水平和结晶年龄等因素的影响,断层和花岗岩等因素的控制能力存在显著差异。然而,针对这种控制能力差异的研究还很缺乏。针对这一空白,我们利用中国高温地热热点川西高原丰富的地质、地球物理和地热数据,提出了一系列证据权重模型。本研究旨在以断层和花岗岩为例,研究不同证据因素控制能力对评价模型的影响。性能指标包括预测率、成功率指数、接收者工作特征曲线(ROC)和地热井预测率。研究结果表明,与传统的证据权重模型相比,通过本研究概述的方法开发的证据权重模型表现出更优越的性能。更高的预测率、成功指数、地热井预测率以及更大的 ROC AUC 值都证明了这种优越性。在这些模型中,同时考虑断层和花岗岩分类的证据权重模型在模型评价指标上表现最佳,在极高潜力区的预测率为 22.528,成功指数为 0.015408。高潜力区的预测率和成功指数分别为 3.656 和 0.0025,中等潜力区的预测率和成功指数分别为 1.649 和 0.001128,AUC 值为 0.808,表明模型具有较好的准确性。在地热井预测方面,基于断层和花岗岩分类证据权重模型的地热有利区总预测率高达 47.0526。因此,在构建证据权重模型时,应充分考虑证据因子差异控制对地热资源形成的影响。这些结果凸显了本研究提出的方法在提高地热资源评估预测精度和可靠性方面的有效性。根据同时考虑断层和花岗岩分类的证据权重模型的预测结果,本文确定了康定、理塘、巴塘和甘孜德格四个地表热量显示丰富的有利地热区。此外,九龙、道孚、碌曲和得荣等地表热量显示相对较弱的地区也显示出较高的地热潜力。今后应重视地热勘探工作。
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A revised weight of evidence model for potential assessments of geothermal resources: a case study at western Sichuan Plateau, China

Efficient exploration of geothermal resources is the basis of exploitation and utilization of geothermal resources. In recent years, Geographic Information System (GIS) has been increasingly used for the exploration owing to its power ability to integrate and analyze multiple sources of data related to the formation of geothermal resources, such as geology, geophysics, and geochemistry. Correctly understanding the control effect of evidence factors on geothermal resources is the premise and basis of whether the prediction results of evidence weight model are accurate. Traditionally, the conventional weight of evidence model assume that each evidence factor exerts a uniform controlling effect on the formation and distribution of geothermal resources. However, recent research indicates significant variations in the controlling ability of factors such as faults and granites, influenced by factors like activity levels and crystalline ages. Yet, studies addressing this differential control are lacking. To address this gap, we propose a series of weight of evidence models using abundant geological, geophysical, and geothermal data from the western Sichuan plateau, a high-temperature geothermal hotspot in China. This study aims to investigate the impact of varying controlling abilities of evidence factors on the evaluation model, with faults and granites as a case. Performance metrics include prediction rate, success rate index, receiver operating characteristic curve (ROC) and prediction rate of geothermal well. The findings of this research reveal that the weight of evidence model developed through the methodology outlined in this study exhibits superior performance compared to the conventional weight of evidence model. This superiority is evidenced by higher prediction rates, success indices, prediction rate of geothermal wells, and larger AUC values of ROC. Among these models, the weight of evidence model considering both fault and granite classification have the best performance in model evaluation indicators, with a prediction rate of 22.528 and a success index of 0.015408 in the very high potential area. The prediction rate and success index of the high potential area are 3.656 and 0.0025, respectively, and the prediction rate and success index of the middle potential area are 1.649 and 0.001128, respectively, and the AUC value is 0.808, indicating that the model has good accuracy. In terms of geothermal well prediction, the total prediction rate of geothermal favorable areas based on fault and granite classification evidence weight model is as high as 47.0526. Therefore, when constructing the weight of evidence model, the influence of the difference control of evidence factors on the formation of geothermal resources should be fully considered. These results underscore the effectiveness of the proposed methodology in enhancing the predictive accuracy and reliability of geothermal resource assessment in this study. Based on the prediction results of the weight of evidence model considering both fault and granite classification, four favorable geothermal areas with abundant surface heat display are identified in this paper, namely Kangding, Litang, Batang and Ganzi-Dege. In addition, the relatively weak surface heat display areas such as Jiulong, Daofu, Luhuo and Derong also show high geothermal potential. Some attention should be paid to geothermal exploration in the future.

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来源期刊
Geothermal Energy
Geothermal Energy Earth and Planetary Sciences-Geotechnical Engineering and Engineering Geology
CiteScore
5.90
自引率
7.10%
发文量
25
审稿时长
8 weeks
期刊介绍: Geothermal Energy is a peer-reviewed fully open access journal published under the SpringerOpen brand. It focuses on fundamental and applied research needed to deploy technologies for developing and integrating geothermal energy as one key element in the future energy portfolio. Contributions include geological, geophysical, and geochemical studies; exploration of geothermal fields; reservoir characterization and modeling; development of productivity-enhancing methods; and approaches to achieve robust and economic plant operation. Geothermal Energy serves to examine the interaction of individual system components while taking the whole process into account, from the development of the reservoir to the economic provision of geothermal energy.
期刊最新文献
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