Graph-based micro-seismic signal classification with an optimised feature space

Classification of seismic events detected from seismic recordings has been gaining popularity in interpretation of subsurface processes, e.g., volcanic systems, earthquake activity, induced seismicity and slope stability, in particular landslides. However, due to the variability of signal representation for different classes in the temporal and spectral space, a large feature space to characterise the uniqueness of a particular type of event is used for classifying seismic signals. The consequence is additional complexity on the classifier and overfitting. So far, there has been little attempt to address dimensionality reduction via feature selection. In this paper, we propose an iterative, alternating graph feature and classifier learning method for micro-seismic signals via graph Laplacian regularization and normalized graph Laplacian regularization. Using recorded micro-seismic events from an active landslide, we demonstrate improved classification accuracy with a relatively small feature space compared to state of the art.