IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2913-d794789.html
   My bibliography  Save this article

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Author

Listed:
  • Yongzhu Xiong

    (School of Geography and Tourism, Jiaying University, Meizhou 514015, China
    Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, China)

  • Mingyong Zhu

    (School of Geography and Tourism, Jiaying University, Meizhou 514015, China
    Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, China)

  • Yongyi Li

    (Institute of Deep Earth Sciences and Green Energy, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China)

  • Kekun Huang

    (Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, China
    School of Mathematics, Jiaying University, Meizhou 514015, China)

  • Yankui Chen

    (School of Geography and Tourism, Jiaying University, Meizhou 514015, China
    Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, China)

  • Jingqing Liao

    (The Eighth Geologic Survey, Guangdong Geological Bureau, Meizhou 514089, China)

Abstract

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F 1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.

Suggested Citation

  • Yongzhu Xiong & Mingyong Zhu & Yongyi Li & Kekun Huang & Yankui Chen & Jingqing Liao, 2022. "Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning," Energies, MDPI, vol. 15(8), pages 1-29, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2913-:d:794789
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2913/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/8/2913/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lanfang He & Ling Chen & Dorji & Xiaolu Xi & Xuefeng Zhao & Rujun Chen & Hongchun Yao, 2016. "Mapping the Geothermal System Using AMT and MT in the Mapamyum (QP) Field, Lake Manasarovar, Southwestern Tibet," Energies, MDPI, vol. 9(10), pages 1-13, October.
    2. Zhang, Yu & Zhang, Yanjun & Yu, Hai & Li, Jianming & Xie, Yangyang & Lei, Zhihong, 2020. "Geothermal resource potential assessment of Fujian Province, China, based on geographic information system (GIS) -supported models," Renewable Energy, Elsevier, vol. 153(C), pages 564-579.
    3. Kumar, Sandeep & Muhuri, Pranab K., 2019. "A novel GDP prediction technique based on transfer learning using CO2 emission dataset," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Wenlong Zhou & Xiangyun Hu & Shilong Yan & Hongdang Guo & Wei Chen & Sijing Liu & Chunyan Miao, 2020. "Genetic Analysis of Geothermal Resources and Geothermal Geological Characteristics in Datong Basin, Northern China," Energies, MDPI, vol. 13(7), pages 1-19, April.
    5. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    6. Shaopeng Huang, 2012. "Geothermal energy in China," Nature Climate Change, Nature, vol. 2(8), pages 557-560, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alvin Kiprono Bett & Saeid Jalilinasrabady, 2021. "Optimization of ORC Power Plants for Geothermal Application in Kenya by Combining Exergy and Pinch Point Analysis," Energies, MDPI, vol. 14(20), pages 1-17, October.
    2. Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
    3. Jianwei Zhao & Zhaofa Zeng & Shuai Zhou & Jiahe Yan & Baizhou An, 2023. "3-D Inversion of Gravity Data of the Central and Eastern Gonghe Basin for Geothermal Exploration," Energies, MDPI, vol. 16(5), pages 1-16, February.
    4. Wanli Gao & Jingtao Zhao & Suping Peng, 2022. "UNet–Based Temperature Simulation of Hot Dry Rock in the Gonghe Basin," Energies, MDPI, vol. 15(17), pages 1-17, August.
    5. Wang, G.Y. & Li, Y.P. & Liu, J. & Huang, G.H. & Chen, L.R. & Yang, Y.J. & Gao, P.P., 2022. "A two-phase factorial input-output model for analyzing CO2-emission reduction pathway and strategy from multiple perspectives – A case study of Fujian province," Energy, Elsevier, vol. 248(C).
    6. Elkadeem, Mohamed R. & Younes, Ali & Mazzeo, Domenico & Jurasz, Jakub & Elia Campana, Pietro & Sharshir, Swellam W. & Alaam, Mohamed A., 2022. "Geospatial-assisted multi-criterion analysis of solar and wind power geographical-technical-economic potential assessment," Applied Energy, Elsevier, vol. 322(C).
    7. Elkadeem, M.R. & Younes, Ali & Sharshir, Swellam W. & Campana, Pietro Elia & Wang, Shaorong, 2021. "Sustainable siting and design optimization of hybrid renewable energy system: A geospatial multi-criteria analysis," Applied Energy, Elsevier, vol. 295(C).
    8. Xiao, Kun & Yu, Bolin & Cheng, Lei & Li, Fei & Fang, Debin, 2022. "The effects of CCUS combined with renewable energy penetration under the carbon peak by an SD-CGE model: Evidence from China," Applied Energy, Elsevier, vol. 321(C).
    9. Pragyan Deb & Davide Furceri & Jonathan D. Ostry & Nour Tawk, 2022. "The Economic Effects of COVID-19 Containment Measures," Open Economies Review, Springer, vol. 33(1), pages 1-32, February.
    10. Chaofeng Wu & Xiangyun Hu & Guiling Wang & Yufei Xi & Wenjing Lin & Shuang Liu & Bo Yang & Jianchao Cai, 2018. "Magnetotelluric Imaging of the Zhangzhou Basin Geothermal Zone, Southeastern China," Energies, MDPI, vol. 11(8), pages 1-15, August.
    11. Mohammadzadeh Bina, Saeid & Jalilinasrabady, Saeid & Fujii, Hikari, 2017. "Energy, economic and environmental (3E) aspects of internal heat exchanger for ORC geothermal power plants," Energy, Elsevier, vol. 140(P1), pages 1096-1106.
    12. Xiaoyang Sun & Baosheng Zhang & Xu Tang & Benjamin C. McLellan & Mikael Höök, 2016. "Sustainable Energy Transitions in China: Renewable Options and Impacts on the Electricity System," Energies, MDPI, vol. 9(12), pages 1-20, November.
    13. Zhang, Bo & Gu, Kai & Shi, Bin & Liu, Chun & Bayer, Peter & Wei, Guangqing & Gong, Xülong & Yang, Lei, 2020. "Actively heated fiber optics based thermal response test: A field demonstration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    14. Altun, A.F. & Kilic, M., 2020. "Thermodynamic performance evaluation of a geothermal ORC power plant," Renewable Energy, Elsevier, vol. 148(C), pages 261-274.
    15. Hu, Ziyuan & Gao, Zongjun & Xu, Xiqiang & Fang, Shaoyan & Zhou, Liangyu & Ji, Deshuai & Li, Fuquan & Feng, Jianguo & Wang, Min, 2022. "Suitability zoning of buried pipe ground source heat pump and shallow geothermal resource evaluation of Linqu County, Shandong Province, China," Renewable Energy, Elsevier, vol. 198(C), pages 1430-1439.
    16. Bayer, Peter & Attard, Guillaume & Blum, Philipp & Menberg, Kathrin, 2019. "The geothermal potential of cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 17-30.
    17. Yang, Haolin & Schell, Kristen R., 2021. "Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets," Applied Energy, Elsevier, vol. 299(C).
    18. Zhao, Yajing & Wang, Jiangfeng, 2016. "Exergoeconomic analysis and optimization of a flash-binary geothermal power system," Applied Energy, Elsevier, vol. 179(C), pages 159-170.
    19. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    20. Wang, Yuqing & Liu, Yingxin & Dou, Jinyue & Li, Mingzhu & Zeng, Ming, 2020. "Geothermal energy in China: Status, challenges, and policy recommendations," Utilities Policy, Elsevier, vol. 64(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2913-:d:794789. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.