Estimation of near-surface temperature in Suwawa Geothermal Prospect, Gorontalo, Sulawesi, Indonesia, based on magnetotelluric and artificial neural network

M. Maryadi, P. Bramanthyo, A. Zarkasyi, H. Mizunaga

研究成果: Contribution to journalConference article査読

抄録

A geophysical survey using broadband magnetotelluric (MT) technology was carried out in Suwawa Geothermal Prospect Area, Gorontalo Province, Sulawesi Island, Indonesia. The target of that research is to evaluate the geothermal potential hidden below the surface, based on underground resistivity distribution. However, the information about resistivity alone is not enough to get a proper understanding of the geothermal system in this area. Another important subsurface feature that could be useful for the evaluation is temperature. In this study, an attempt to predict the subsurface temperature using resistivity and limited information from a shallow borehole thermogram was carried out. Employing the dependency between resistivity and temperature an indirect temperature estimator was built, thanks to the applicability of artificial neural network (ANN) to learn the pattern connecting both parameters. Comparing some neural network training data shows that the predictive powers of the calibrated neural network highly influenced by the geological difference between the location of borehole and MT station. The best trained ANN was then used to predict the temperature below the other MT stations. The result shows that a proper ANN architecture is important to improve the deeper temperature estimation. The best ANN estimator was obtained from the BT01 and AMT39 data pair, which has the highest correlation as well. This preliminary study gives useful insight into how resistivity could be an alternative tool to delineate the near-surface temperature profile, in order to get a more comprehensive image of the subsurface geothermal system.

本文言語英語
論文番号012018
ジャーナルIOP Conference Series: Earth and Environmental Science
851
1
DOI
出版ステータス出版済み - 10 25 2021
イベント2021 International Conference on Geological Engineering and Geosciences, ICGoES 2021 - Yogyakarta, Virtual, インドネシア
継続期間: 3 16 20213 18 2021

All Science Journal Classification (ASJC) codes

  • 環境科学(全般)
  • 地球惑星科学(全般)

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