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Machine-Learning Seismic Imaging Reveals How Supercritical Volcanic Fluids Influence Earthquakes

The peer-reviewed work links rainfall-driven groundwater pressure to bursts of seismicity in Kyushu.

Overview

  • Researchers mapped deep fluid systems in 3D beneath the Kuju region of Kyushu, resolving the brittle–ductile transition where fluids accumulate.
  • The study shows that high-pressure supercritical fluids can be trapped, migrate through permeable windows and fractures, and undergo phase changes that alter fault stress.
  • Dense seismometer data combined with machine learning detailed earthquake distributions and mechanisms beyond the resolution of earlier electromagnetic surveys.
  • Analyses identified reservoirs sealed beneath cap layers, pathways that vent fluids upward, and a clear statistical correlation between heavy rain and increased seismicity.
  • The findings, published in Communications Earth & Environment, suggest improvements to hazard models and geothermal site targeting, with commercial use constrained by extreme deep-drilling challenges; the work was supported by NEDO and JSPS.