Unsupervised clustering of mining-induced microseismicity provides insights into source mechanisms

Microseismic source mechanisms in underground mines can provide information about the rock mass response to mining. Conventional approaches to such studies rely upon moment tensor solutions that are susceptible to modeling assumptions and need reliable information about source locations and high-resolution velocity models. We propose the application of unsupervised clustering to group microseismic events into different classes directly from the waveform data such that the events in a specific class have similar source mechanisms. Our method has three main steps, first using spectral decomposition to separate the source terms from the path-receiver contributions in the observed amplitude spectra of events occurring in spatially dense clusters. Second, reducing the number of features from the source spectra using independent component analysis (ICA). Third, applying a Gaussian mixture model (GMM) to the reduced feature matrix to obtain event clusters. To test our method, we generate synthetic waveform data using the receiver network and the recorded microseismic event locations in an underground potash mine in Saskatchewan. Results show the ability of our method to separate events into different classes corresponding to differences in source mechanisms. Application to field data recorded in the mine during February 2021 successfully discriminates between blasts and microseismic events. The data recorded between 1 March and 30 June 2021 that contain microseismic events only are divided into two dominant classes. Using known moment tensors (MT) of some of these events for labeling, we interpret one of the two classes as having dominant double-couple mechanisms as compared to the other which most likely corresponds to the linear dipole-tensile mechanisms. Our method, combined with some expert knowledge such as MT of some larger magnitude events, can offer an assessment of source types of large microseismic populations as often encountered in induced seismicity. • A clustering approach is developed to group microseismic events based on source mechanisms. • The developed method works directly on three-component (3C) waveform data. • It can separate blasts from microseismicity and group events based on source types.