Seismic Sensors Detect Forests Via Ambient Noise
A Feb. 10, 2026 arXiv preprint by Marat Latypov and colleagues demonstrates passive seismic sensing can detect forests from ambient seismic noise. Using Alaska seismic data, supervised models classify forest presence with 86% accuracy and identify key 35–60 Hz discriminating frequencies, while cross-correlations approximate empirical Green's functions. A topological acoustics analysis confirms the physical basis, offering scalable all-weather vegetation monitoring.
Key Points
- 1Achieve 86% classification accuracy detecting forests from ambient seismic noise in Alaska
- 2Show physics basis: cross-correlations approximate empirical Green's functions, confirming tree-wave interaction signatures
- 3Enable all-weather, persistent vegetation monitoring using passive seismic arrays for scalable environmental change tracking
Scoring Rationale
Novel, directly usable seismic-ML method for forest detection; limited by a single arXiv preprint and region-specific data.
Sources
Public references used for this report.
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