倾听森林中的树木：利用机器学习和拓扑声学进行远程传感的地震噪声
Hearing the forest for the trees: machine learning and topological acoustics for remote sensing with seismic noise
AI总结：
利用机器学习和拓扑声学分析地震噪声，实现对森林的持续监测，准确率86%，识别关键频率范围，提供环境变化的稳健解决方案。
摘要：
监测远程森林是一个全球性挑战，对于气候缓解和生物多样性保护至关重要，但卫星观测经常受到天气、密集树冠和太阳依赖性的限制。在这里，我们展示了被动地震传感提供了一种持续、全天候的替代方案，用于自主生态系统监测，通过捕捉环境波场中树木的特征可学习签名。利用阿拉斯加的地震数据，我们证明了站间互相关提供了森林检测的物理基础，通过近似介质的经验格林函数。对这些数据应用监督机器学习模型，实现了86%的分类准确率，识别出关键区分频率（35至60Hz），与已知的森林-波相互作用一致。对几何相位变化的拓扑声学分析独立验证了这些数据驱动分类的物理来源。共同，这些结果提供了首次证明微妙的森林-波相互作用在环境地震噪声中显现，并可以作为持续植被监测的可扩展工具的演示，为跟踪环境变化的挑战地区提供稳健的解决方案。
英文摘要：
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.
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