Ambient Noise Tomography and Machine Learning Models to Reveal Geothermal Structure in the Taupo Volcanic Zone

Abstract

To reveal potential geothermal fluid flows and temperature distribution in the Taupo Volcanic Zone, North Island of the New Zealand Hikurangi subduction zone, we analyzed seismometer and well log data. First, we extracted Rayleigh phase velocity dispersion curves from ambient noise cross-correlation analysis and then estimated S-wave velocity models through 3D surface wave tomography. Second, we constructed the 3D temperature model derived from our machine learning models using 3D velocity and temperature log data. Faulted/fractured zones, which can host fluids or magma, led to significant reductions in S-wave velocity within the subsurface. As a result, our S-wave velocities were lower in existing geothermal reservoirs and through flow pathways (i.e., active fault zones), particularly within the NE-SW directional structure toward Lake Taupo. The most suitable 3D temperature model (or others) was obtained based on the Gaussian process regression model, compared to other models in all machine learning algorithms. High-temperature areas ranging up to 300 °C or more are consistent with flow paths through the structure. Our approach could contribute to the unrevealed geothermal structures and fluid flow pathways in this region.