Hearing the forest for the trees: machine learning and topological acoustics for remote sensing with seismic noise
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915810781429760 |
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| author | Wang, Jiayang Huang, I-Tzu Luo, Bingxu Beck, Susan L. Huettmann, Falk DeVaughn, Skyler Stilin, Benjamin Runge, Keith Deymier, Pierre Latypov, Marat I. |
| author_facet | Wang, Jiayang Huang, I-Tzu Luo, Bingxu Beck, Susan L. Huettmann, Falk DeVaughn, Skyler Stilin, Benjamin Runge, Keith Deymier, Pierre Latypov, Marat I. |
| contents | Monitoring remote forests is a global challenge central to climate mitigation and biodiversity conservation, yet satellite observations are frequently limited by weather, dense canopies, and solar dependency. Here we show that passive seismic sensing offers a persistent, all-weather alternative for autonomous ecosystem monitoring by capturing characteristic learnable signatures of trees within the ambient wavefield. Using seismic data from Alaska, we demonstrate that cross-correlations between stations provide a physical basis for forest detection by approximating the empirical Green's function of the medium. Supervised machine learning models applied to these data achieve a classification accuracy of 86%, identifying key discriminating frequencies (35 to 60 Hz) consistent with known forest-wave interactions. A topological acoustics analysis of the geometric phase change independently confirms the physical origin of these data-driven classifications. Together, these results provide the first demonstration that subtle forest-wave interactions manifest in ambient seismic noise and can be harnessed as a scalable tool for continuous vegetation monitoring, offering a robust solution for tracking environmental change challenging regions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_18485 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hearing the forest for the trees: machine learning and topological acoustics for remote sensing with seismic noise Wang, Jiayang Huang, I-Tzu Luo, Bingxu Beck, Susan L. Huettmann, Falk DeVaughn, Skyler Stilin, Benjamin Runge, Keith Deymier, Pierre Latypov, Marat I. Geophysics Data Analysis, Statistics and Probability Monitoring remote forests is a global challenge central to climate mitigation and biodiversity conservation, yet satellite observations are frequently limited by weather, dense canopies, and solar dependency. Here we show that passive seismic sensing offers a persistent, all-weather alternative for autonomous ecosystem monitoring by capturing characteristic learnable signatures of trees within the ambient wavefield. Using seismic data from Alaska, we demonstrate that cross-correlations between stations provide a physical basis for forest detection by approximating the empirical Green's function of the medium. Supervised machine learning models applied to these data achieve a classification accuracy of 86%, identifying key discriminating frequencies (35 to 60 Hz) consistent with known forest-wave interactions. A topological acoustics analysis of the geometric phase change independently confirms the physical origin of these data-driven classifications. Together, these results provide the first demonstration that subtle forest-wave interactions manifest in ambient seismic noise and can be harnessed as a scalable tool for continuous vegetation monitoring, offering a robust solution for tracking environmental change challenging regions. |
| title | Hearing the forest for the trees: machine learning and topological acoustics for remote sensing with seismic noise |
| topic | Geophysics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2602.18485 |