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Audio-based Unsupervised Machine Learning Reveals Cyclic Changes in Seismicity Mechanisms in the Geysers Geothermal Field, California
Benjamin K. HOLTZMAN, Arthur PATE, John PAISLEY, Felix WALDHAUSER, Douglas REPETTO
[Strabo Engineering, USA]
The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram not the seismogram. Clustering of 46,000 earthquakes of $0.3
Topic: GeophysicsGo back