Typical mining operations can induce microseismicity and in some cases can result in the occurrence of moderate to large events, which is an expected but not always fully understood phenomenon. To assess the safety and efficiency of mining operations, operators must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of induced microseismicity from a potash mine in Saskatchewan, Canada, are analyzed and quantified. Specifically, the frequency-magnitude statistics display a rich behavior that deviates from the standard Gutenberg-Richter scaling for small magnitudes. To model the magnitude distribution, we consider two additional models, i.e. the tapered Pareto distribution and a mixture of the tapered Pareto and Pareto distributions to fit the bi-modal catalog data. We also observe deviations from the Poisson statistics on short-time scales that are primarily driven by mining operations. To study the clustering aspects of the observed microseismicity, the nearest-neighbor distance (NND) method is applied. This allowed us to identify characteristics of the clusters of micro-events and to analyze their structure in space, time and magnitude domains. The implemented modeling approaches and obtained results can be used to further advance strategies and protocols for the safe and efficient operation of potash mines.

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