EGU24-3862, updated on 08 Mar 2024
https://doi.org/10.5194/egusphere-egu24-3862
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Integrated geothermal exploration of Hongchailin geothermal field in Taiwan using seismic velocity and resistivity tomography with unsupervised learning analysis

Hong-Mao Huang1, Hsin-Hua Huang1,2, Yung-en Yu3, Gong-Ruei Ho1, Min-Hung Shih4, Ya-Chuan Lai4, Po-Li Su1, Hung-Yu Yen1, Tsung-Chih Chi1, Cheng-Horng Lin1, Jian-Cheng Lee1, Yue-Gau Chen5, and Sun-Lin Chung1
Hong-Mao Huang et al.
  • 1Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan
  • 2Department of Geosciences, National Taiwan University, Taipei, Taiwan
  • 3CPC Corporation, Kaohsiung, Taiwan
  • 4National Center for Research on Earthquake Engineering, Taipei, Taiwan
  • 5Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan

Geothermal energy serves as one of sustainable and low-emission energy sources with the potential to mitigate climate change and enhance energy security. It offers a viable substitute for conventional fossil fuels or electrical energy. The Hongchailin area in Ilan, Taiwan has been considered as a potential geothermal energy field in recent years. To investigate possible geothermal sources in Hongchailin, a dense seismic array comprising 186 geophones is deployed over a 5 × 4 km area covering the probable geothermal field between August 2022 and January 2023. A vibroseis experiment was operated along multiple lines across the array with 12-second sweep-frequency signals from 6 to 96 Hz. To retrieve clear vibroseis-generated P-wave arrivals, we first remove the sweep signals from the raw waveforms by the cross-correlation method, and stack the processed waveforms from successive co-site shots with the Phase-Weighted Stacking (PWS) method to improve the signal-to-noise ratio. We use the Recursive-STA/LTA method for P-wave arrival picking. Visual inspection and additional criteria are made for confirming and refining the accuracy of P-arrivals. Lastly, a total of 41,095 P-arrivals are collected and used for seismic tomographic inversion. The velocity model shows several velocity anomaly zones in good spatial correlation with the resistivity model, although the resolvable depth of the model is limited to ~1 km. It demonstrates the active-source seismic tomography as a valuable geothermal exploration tool. Further, we employ unsupervised learning methods to classify and explore the resistivity-velocity relationships in each cluster. The preliminary results indicate a positive linear correlation for some regions but negative for some others, implying different materials such as rock composition or fluid content. These findings provide valuable insights for comprehensive understanding of geothermal resources in the Hongchailin area.

How to cite: Huang, H.-M., Huang, H.-H., Yu, Y., Ho, G.-R., Shih, M.-H., Lai, Y.-C., Su, P.-L., Yen, H.-Y., Chi, T.-C., Lin, C.-H., Lee, J.-C., Chen, Y.-G., and Chung, S.-L.: Integrated geothermal exploration of Hongchailin geothermal field in Taiwan using seismic velocity and resistivity tomography with unsupervised learning analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3862, https://doi.org/10.5194/egusphere-egu24-3862, 2024.

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