Imaging of potential geothermal structures in Japan,
New Zealand, and Kenya inferred from ambient noise
and machine learning approaches
Chanmaly Chhun (Kyushu Univ. & Univ. Tokyo)
Takeshi Tsuji (Kyushu Univ. & Univ. Tokyo)
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Conclusions
By applying ambient noise analysis and our machine learning models to seismometer and/or borehole data, we can obtain the key findings as follows:
In Paka - Silali volcanoes, Kenya:
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In Taupo volcanic zone, New Zealand:
Conclusions
3D S-wave velocity model >> 3D Temperature model
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In Kuju volcanoes, Japan:
Conclusions
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This study was supported by the Science and Technology Research Partnership for Sustainable Development (SATREPS) & The New Energy and Industrial Technology Development Organization (NEDO), Japan, and partially supported by the Japan Society for the Promotion of Science (KAKENHI grant JP20H01997, JP21H05202, and JP22H05108).
We are greatly indebted to Yasuhiro Fujimitsu (Kyushu Univ.) for the SATREPS project supervision & support.
We thank the IRIS Data Management Center (IRISDMC) http://www.fdsn.org/networks/detail/1C_2011/ for providing the open seismometer data from Kenya (Velasco et al., 2011) and from Bannister (2009) or http://fdsn.adc1.iris.edu/networks/detail/Z8_2009/.
A deep appreciation goes to Koichiro Watanabe (JICA senior advisor) for introducing me geothermal geology and volcanology during field itinerary to the UNZEN-ASO-KUJU volcanoes on October 8-10, 2022.
Seismicity is from JMA (https://www.data.jma.go.jp/svd/eqev/data/bulletin/hypo_e.html) and Andajani et al., (2023).
Great thanks to Fernando Lawrence, Bokani Nthaba, and Tsuji labmates for technical helps.
Acknowledgements