Hearing the forest for the trees: machine learning and topological acoustics for remote sensing with seismic noise

TL;DR

This paper demonstrates that passive seismic sensing combined with machine learning and topological acoustics can effectively monitor forests remotely, providing an all-weather, continuous alternative to satellite observations.

Contribution

It introduces a novel method using ambient seismic noise and machine learning for forest detection, validated with topological acoustics analysis, advancing ecosystem monitoring techniques.

Findings

Seismic cross-correlations can detect forests by approximating Green's functions.

Machine learning models achieve 86% accuracy in forest classification.

Topological acoustics confirms the physical basis of seismic forest detection.

Abstract

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.