Abstract Soil nature and water saturation of the near‐surface directly influence land use resilience and sustainability in urban areas facing intense climate forcing and human activity. Seismic methods are widely applied in this context for subsurface characterization and monitoring, but often fall short in delivering joint geological, hydrogeological, and geomechanical descriptions. We explore the effectiveness of a passive seismic approach, coupled with artificial intelligence (AI), to characterize geological structures and capture groundwater dynamics. We present a deterministic inversion technique powered by a language model, to translates seismic wave velocity measurements into petrophysical parameters in the form of textual descriptions. Results successfully delineate subsurface structures with their respective composition and mechanical characteristics, while accurately predicting daily water table levels. Validation demonstrates high accuracy, with a normalized root‐mean‐square error of 8%, while delivering fast insights into subsurface conditions. This underscores the potential of AI to enhance subsurface characterization across multiple scales.
Observations from the Little Bunker
Last updated: August 18, 2025 04:13 PM
Atmospheric CO₂
425.4 ppm
+18.4
ppm/year
Atmospheric CH₄
1934.91 ppb
+9.11
ppb/year
Global Temperature
+1.48 °C
Peak: +1.73°C
World Population
8.1 billion
+67
million/year