IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v294y2024ics0360544224006194.html
   My bibliography  Save this article

A knowledge guided deep learning framework for underground natural gas micro-leaks detection from hyperspectral imagery

Author

Listed:
  • Du, Ying
  • Jiang, Jinbao
  • Yu, Zijian
  • Liu, Ziwei
  • Pan, Yingyang
  • Xiong, Kangni

Abstract

Detecting natural gas (NG) micro-leaks is critical for NG transport and storage. In the event of underground NG leaks in vegetation covered areas, a priori knowledge derived from analyzing vegetation growth mechanisms can effectively describe the degree of vegetation response to NG stress, which is of theoretical importance for the quantitative detection of NG leaks. This study proposed a knowledge-guided deep learning framework and use it for underground NG leaks detection, in which an image and spectrum combined multi-branching deep learning (DL) network (Img-Spec-PGE model) was used to estimate the leaf area index (LAI) of NG-stressed plants, and an improved physical model for describing plant NG stress mechanisms was proposed, then assimilating the SAFY-NG model with LAI as an intermediate variable to obtain an accurate NG stress factor (Ka). The results showed that the Img-Spec-PGE model with the NIR-RE-R spectral bands' combination had the highest LAI estimation accuracy (R2 = 0.867, RMSE = 0.312) among the three designed DL models. The assimilated SAFY-NG model can accurately simulate the wheat LAI growth curve under NG leaks, and the calibration results of environmental parameters for NG leaks (leak start time and leak point depth) were consistent with the realistic experimental settings. Ka is sensitive to NG leaks in all experimental periods (p < 0.05). Enabling distinction between leak and non-leak scenarios as early as 1 day after NG leaks. 30 days after NG leaks, Ka can distinguish between 1 L min−1 and 1.5 L min−1 gas flow. The proposed approach provides a reference for detecting micro-leaks in NG transit and storage.

Suggested Citation

  • Du, Ying & Jiang, Jinbao & Yu, Zijian & Liu, Ziwei & Pan, Yingyang & Xiong, Kangni, 2024. "A knowledge guided deep learning framework for underground natural gas micro-leaks detection from hyperspectral imagery," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006194
    DOI: 10.1016/j.energy.2024.130847
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224006194
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.130847?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Jinbao & Steven, Michael D. & He, Ruyan & Chen, Yunhao & Du, Peijun, 2016. "Identification of plants responding to CO2 leakage stress using band depth and the full width at half maxima of canopy spectra," Energy, Elsevier, vol. 100(C), pages 73-81.
    2. Luna, Marcos & Nicholas, Dominic, 2022. "An environmental justice analysis of distribution-level natural gas leaks in Massachusetts, USA," Energy Policy, Elsevier, vol. 162(C).
    3. Chen, Yun & Guerschman, Juan P & Cheng, Zhibo & Guo, Longzhu, 2019. "Remote sensing for vegetation monitoring in carbon capture storage regions: A review," Applied Energy, Elsevier, vol. 240(C), pages 312-326.
    4. Battude, Marjorie & Al Bitar, Ahmad & Brut, Aurore & Tallec, Tiphaine & Huc, Mireille & Cros, Jérôme & Weber, Jean-Jacques & Lhuissier, Ludovic & Simonneaux, Vincent & Demarez, Valérie, 2017. "Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery," Agricultural Water Management, Elsevier, vol. 189(C), pages 123-136.
    5. Yang, Zhenfeng & Tian, Juncang & Wang, Zhi & Feng, Kepeng & Ouyang, Zan & Zhang, Lixin & Yan, Xinfang, 2023. "Coupled soil water stress and environmental effects on changing photosynthetic traits in wheat and maize," Agricultural Water Management, Elsevier, vol. 282(C).
    6. Jiang Jinbao & Michael D Steven & Cai Qingkong & He Ruyan & Guo Haiqiang & Chen Yunhao, 2014. "Detecting bean stress response to CO 2 leakage with the utilization of leaf and canopy spectral derivative ratio," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 4(4), pages 468-480, 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. Segovia-Cardozo, Daniel Alberto & Rodríguez-Sinobas, Leonor & Zubelzu, Sergio, 2019. "Water use efficiency of corn among the irrigation districts across the Duero river basin (Spain): Estimation of local crop coefficients by satellite images," Agricultural Water Management, Elsevier, vol. 212(C), pages 241-251.
    2. Tsakmakis, I.D. & Gikas, G.D. & Sylaios, G.K., 2021. "Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize," Agricultural Water Management, Elsevier, vol. 255(C).
    3. João Lucas Della-Silva & Carlos Antonio da Silva Junior & Mendelson Lima & Paulo Eduardo Teodoro & Marcos Rafael Nanni & Luciano Shozo Shiratsuchi & Larissa Pereira Ribeiro Teodoro & Guilherme Fernand, 2022. "CO 2 Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia," Sustainability, MDPI, vol. 14(9), pages 1-15, May.
    4. Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
    5. Grusson, Youen & Wesström, Ingrid & Joel, Abraham, 2021. "Impact of climate change on Swedish agriculture: Growing season rain deficit and irrigation need," Agricultural Water Management, Elsevier, vol. 251(C).
    6. Quanfeng Li & Wei Liu & Guoming Du & Bonoua Faye & Huanyuan Wang & Yunkai Li & Lu Wang & Shijin Qu, 2022. "Spatiotemporal Evolution of Crop Planting Structure in the Black Soil Region of Northeast China: A Case Study in Hailun County," Land, MDPI, vol. 11(6), pages 1-14, May.
    7. Zhang, Chao & Liu, Jiangui & Shang, Jiali & Dong, Taifeng & Tang, Min & Feng, Shaoyuan & Cai, Huanjie, 2021. "Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model," Agricultural Water Management, Elsevier, vol. 255(C).
    8. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2018. "Estimating cotton water consumption using a time series of Sentinel-2 imagery," Agricultural Water Management, Elsevier, vol. 207(C), pages 44-52.
    9. Corbari, C. & Ben Charfi, I. & Al Bitar, A. & Skokovic, D. & Sobrino, J.A. & Perelli, C. & Branca, G. & Mancini, M., 2022. "A fully coupled crop-water-energy balance model based on satellite data for maize and tomato crops yield estimates: The FEST-EWB-SAFY model," Agricultural Water Management, Elsevier, vol. 272(C).
    10. Chen, Yun & Guerschman, Juan P & Cheng, Zhibo & Guo, Longzhu, 2019. "Remote sensing for vegetation monitoring in carbon capture storage regions: A review," Applied Energy, Elsevier, vol. 240(C), pages 312-326.
    11. Jiang, Jinbao & Steven, Michael D. & He, Ruyan & Chen, Yunhao & Du, Peijun, 2016. "Identification of plants responding to CO2 leakage stress using band depth and the full width at half maxima of canopy spectra," Energy, Elsevier, vol. 100(C), pages 73-81.
    12. Hamze, Mohamad & Cheviron, Bruno & Baghdadi, Nicolas & Lo, Madiop & Courault, Dominique & Zribi, Mehrez, 2023. "Detection of irrigation dates and amounts on maize plots from the integration of Sentinel-2 derived Leaf Area Index values in the Optirrig crop model," Agricultural Water Management, Elsevier, vol. 283(C).
    13. Sara Yasemi & Yasin Khalili & Ali Sanati & Mohammadreza Bagheri, 2023. "Carbon Capture and Storage: Application in the Oil and Gas Industry," Sustainability, MDPI, vol. 15(19), pages 1-32, October.
    14. Han, Congying & Zhang, Baozhong & Chen, He & Liu, Yu & Wei, Zheng, 2020. "Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data," Agricultural Water Management, Elsevier, vol. 240(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:eee:energy:v:294:y:2024:i:c:s0360544224006194. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.