Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

中文翻译：

---

使用无监督学习识别不同类别的地震噪声信号

正确分类非构造地震信号对于检测微地震和增进对正在进行的弱地面运动的理解至关重要。我们使用无监督机器学习来标记连续波形中常见的五类非平稳地震噪声。描述数据的时间和频谱特征会聚在一起，以识别出突发和脉冲波形的可分离类型。训练过的聚类模型用于对站点间距为10-30 m的密集地震阵列中每1 s连续地震记录进行分类。我们表明，主要的噪声信号可以高度本地化，并在数百米的长度范围内变化。该方法论证明了弱地震动的复杂性，并提高了低信噪比的地震波形分析标准。