Using unsupervised machine learning for clustering seismic noise: a case study of a dense seismic array at the Weifang segment of the Tanlu Fault

Ground motion induced by non-tectonic sources is commonly regarded as seismic noise in seismic recording. Seismic noise has similar characteristics of weak tectonic signals such as microearthquakes or nonvolcanic tremors, making the recognition and detection of weak tectonic signals very challenging with traditional signal processing techniques. Lack of knowledge on seismic noise features can also negatively affect model training when applying the latest machine learning techniques for weak signal detection. Thus, it is necessary to conduct investigations on seismic noise, understanding its features and potential sources. In this study, we apply the K-means analysis to continuous recordings of a dense seismic array deployed at the Weifang segment of the Tanlu fault in north China to characterize various types of seismic noise. The analysis indicates that noise field recorded by the array comprises 6 types of signals related to various nontectonic sources, including road and rail traffic, wind, and nearby power lines.