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Mapping the Geothermal System Using AMT and MT in the Mapamyum (QP) Field, Lake Manasarovar, Southwestern Tibet

Author

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  • Lanfang He

    (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China)

  • Ling Chen

    (State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
    Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100029, China)

  • Dorji

    (Tibet Bureau of Exploration and Development of Geology and Mineral Resources, Lasa 850000, China)

  • Xiaolu Xi

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Xuefeng Zhao

    (Information and Geophysics Institute, Central South University, Changsha 410073, China)

  • Rujun Chen

    (Information and Geophysics Institute, Central South University, Changsha 410073, China)

  • Hongchun Yao

    (Information and Geophysics Institute, Central South University, Changsha 410073, China)

Abstract

Southwestern Tibet plays a crucial role in the protection of the ecological environment and biodiversity of Southern Asia but lacks energy in terms of both power and fuel. The widely distributed geothermal resources in this region could be considered as potential alternative sources of power and heat. However, most of the known geothermal fields in Southwestern Tibet are poorly prospected and currently almost no geothermal energy is exploited. Here we present a case study mapping the Mapamyum (QP) geothermal field of Southwestern Tibet using audio magnetotellurics (AMT) and magnetotellurics (MT) methods. AMT in the frequency range 11.5–11,500 Hz was used to map the upper part of this geothermal reservoir to a depth of 1000 m, and MT in the frequency range 0.001–320 Hz was used to map the heat source, thermal fluid path, and lower part of the geothermal reservoir to a depth greater than 1000 m. Data from 1300 MT and 680 AMT stations were acquired around the geothermal field. Bostick conversion with electromagnetic array profiling (EMAP) filtering and nonlinear conjugate gradient inversion (NLCGI) was used for data inversion. The AMT and MT results presented here elucidate the geoelectric structure of the QP geothermal field, and provide a background for understanding the reservoir, the thermal fluid path, and the heat source of the geothermal system. We identified a low resistivity anomaly characterized by resistivity in the range of 1–8 Ω∙m at a depth greater than 7 km. This feature was interpreted as a potential reflection of the partially melted magma in the upper crust, which might correlate to mantle upwelling along the Karakorum fault. It is likely that the magma is the heat source of the QP geothermal system, and potentially provides new geophysical evidence to understand the occurrence of the partially melted magmas in the upper crust in Southwestern Tibet.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:855-:d:81155
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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