3:30 PM - 3:49 PM
[SVC30-05] Imaging of potential geothermal structures in Japan, New Zealand, and Kenya inferred from ambient noise and machine learning approaches
Keywords:ambient noise imaging , S-wave velocity model, seismic anisotropy, geothermal structure , machine learning
We revealed seismic velocity, anisotropic structure, and temperature models of potential geothermal systems in Kenya, New Zealand, and Japan based on our ambient noise imaging and machine learning approaches. The S-wave velocity (Vs) is significantly reduced due to magmatic or fluid-hosted fracture systems. First, we extracted Rayleigh phase velocity dispersion curves from ambient noise cross-correlation analysis and then estimated the S-wave velocity structure by 3D surface wave tomography. Second, we constructed the 3D anisotropic model derived from Rayleigh wave phase velocity tomography. Using our high-resolution velocity model and well data, we then can estimate rock physical properties (e.g., 3D temperature) through our machine learning models.
In the Silali – Paka volcanic area (20km apart), Kenya, geothermal-volcanic systems are related to continental drift. We used seismometers to obtain the 3D Vs structure. As a result, the magma chambers (Low Vs anomaly) beneath Paka and Silali volcanoes are at depths > 3 - 4km from the surface. Additionally, the geothermal structure, which potentially hosted fluids, is located above magma chambers and fractured systems below the rift floor of the volcanic axis.
In Taupo Volcanic Zone, New Zealand, geothermal-volcanic systems are related to the volcanic rift zone off Hikurangi oblique subduction. We found geothermal systems and potential unrevealed reservoirs along the flow pathways (i.e., active fault map) within complex caldera systems. Furthermore, we can detect zones of frequent seismicity occurrences triggered by natural fluid flows and/or geothermal power stations within the boundary of high- and low-Vs zones. Then, the 3D Vs model and 7 Ngatamariki borehole data were used to estimate the 3D temperature model based on machine learning in this study area. Hot fluid flows are correspondent to the flow pathways, and supercritical ones >300oC can be at depth just greater than 2 km from the surface.
In Kuju volcano, Japan, geothermal-volcanic systems are related to the graben-depression zone caused by tectonic lines around the center of Kyushu Island off Nankai oblique subduction. We revealed existing hydrothermal fluids systems beneath the Otake and Hatchobaru geothermal power stations in Japan. The heat source is supplied from Mt. Kuroiwa, which is continuously directed by the trending structure of the NE-SW faults toward the geothermal power stations. These trending structures and other geothermal fluid flow structures were revealed in high resolution based on our azimuthal anisotropic results.
Our approach based on ambient noise can map geothermal structures in high resolution, which is of great interest for geothermal power companies and research, as well as the construction of a 3D velocity model to detect seismicity and reservoir monitoring.
This work was supported by Leading Enhanced Notable Geothermal Optimization (LENGO) of SATREPS (JICA-JST), and 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).
In the Silali – Paka volcanic area (20km apart), Kenya, geothermal-volcanic systems are related to continental drift. We used seismometers to obtain the 3D Vs structure. As a result, the magma chambers (Low Vs anomaly) beneath Paka and Silali volcanoes are at depths > 3 - 4km from the surface. Additionally, the geothermal structure, which potentially hosted fluids, is located above magma chambers and fractured systems below the rift floor of the volcanic axis.
In Taupo Volcanic Zone, New Zealand, geothermal-volcanic systems are related to the volcanic rift zone off Hikurangi oblique subduction. We found geothermal systems and potential unrevealed reservoirs along the flow pathways (i.e., active fault map) within complex caldera systems. Furthermore, we can detect zones of frequent seismicity occurrences triggered by natural fluid flows and/or geothermal power stations within the boundary of high- and low-Vs zones. Then, the 3D Vs model and 7 Ngatamariki borehole data were used to estimate the 3D temperature model based on machine learning in this study area. Hot fluid flows are correspondent to the flow pathways, and supercritical ones >300oC can be at depth just greater than 2 km from the surface.
In Kuju volcano, Japan, geothermal-volcanic systems are related to the graben-depression zone caused by tectonic lines around the center of Kyushu Island off Nankai oblique subduction. We revealed existing hydrothermal fluids systems beneath the Otake and Hatchobaru geothermal power stations in Japan. The heat source is supplied from Mt. Kuroiwa, which is continuously directed by the trending structure of the NE-SW faults toward the geothermal power stations. These trending structures and other geothermal fluid flow structures were revealed in high resolution based on our azimuthal anisotropic results.
Our approach based on ambient noise can map geothermal structures in high resolution, which is of great interest for geothermal power companies and research, as well as the construction of a 3D velocity model to detect seismicity and reservoir monitoring.
This work was supported by Leading Enhanced Notable Geothermal Optimization (LENGO) of SATREPS (JICA-JST), and 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).