TY - JOUR
T1 - Subsurface temperature estimation in a geothermal field based on audio-frequency magnetotelluric data
AU - Maryadi, Maryadi
AU - Mizunaga, Hideki
N1 - Funding Information:
We wish to thank the Indonesian Endowment Fund for Education (LPDP) for the financial support of the study.
Publisher Copyright:
© 2021 Australian Society of Exploration Geophysicists.
PY - 2022
Y1 - 2022
N2 - In geothermal exploration, the subsurface temperature distribution is an important parameter since it is closely related to the existing geothermal system. However, subsurface temperature information can only be obtained from boreholes, requiring a considerable cost and a limited number. A new geothermometry technique based on the relationship between temperature and resistivity has been proposed to overcome this difficulty. In order to enhance the reliability of this method, the subsurface heat condition in a different geological and geographical location was reconstructed. The temperature up to some depth beneath the surface could be estimated incorporating the back-propagation neural network using resistivity determined by high-resolution audio-magnetotelluric soundings. The temperature profiles resulting from the neural network fit with those observed from nearby boreholes, providing a satisfying evaluation result of the model’s predictive powers. Joint interpretation was then carried out following the one-dimensional resistivity inversion result and temperature cross-section. Both resistivity and temperature anomalies show an excellent agreement, so that subsurface anomaly such as altered layers, reservoir zone, and some possible faults was detected. With an appropriate training strategy, this technique can help to significantly reduce the costs at estimating the subsurface temperature.
AB - In geothermal exploration, the subsurface temperature distribution is an important parameter since it is closely related to the existing geothermal system. However, subsurface temperature information can only be obtained from boreholes, requiring a considerable cost and a limited number. A new geothermometry technique based on the relationship between temperature and resistivity has been proposed to overcome this difficulty. In order to enhance the reliability of this method, the subsurface heat condition in a different geological and geographical location was reconstructed. The temperature up to some depth beneath the surface could be estimated incorporating the back-propagation neural network using resistivity determined by high-resolution audio-magnetotelluric soundings. The temperature profiles resulting from the neural network fit with those observed from nearby boreholes, providing a satisfying evaluation result of the model’s predictive powers. Joint interpretation was then carried out following the one-dimensional resistivity inversion result and temperature cross-section. Both resistivity and temperature anomalies show an excellent agreement, so that subsurface anomaly such as altered layers, reservoir zone, and some possible faults was detected. With an appropriate training strategy, this technique can help to significantly reduce the costs at estimating the subsurface temperature.
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U2 - 10.1080/08123985.2021.1949945
DO - 10.1080/08123985.2021.1949945
M3 - Article
AN - SCOPUS:85109872102
VL - 53
SP - 275
EP - 288
JO - Exploration Geophysics
JF - Exploration Geophysics
SN - 0071-3473
IS - 3
ER -