Neural Machine Translation of Seismic Ambient Noise for Soil Nature and Water Saturation Characterization
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...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2025GL114852 |
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| Summary: | 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. |
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| ISSN: | 0094-8276 1944-8007 |