The forecasting of the evolution of natural hazards is an important and critical problem in natural sciences and engineering. Earthquake forecasting is one such example and is a difficult task due to the complexity of the occurrence of earthquakes. Since earthquake forecasting is typically based on the seismic history of a given region, the analysis of the past seismicity plays a critical role in modern statistical seismology. In this respect, the recent three significant mainshocks that occurred in Alaska (the 2002, Mw 7.9 Denali; the 2018, Mw 7.9 Kodiak; and the 2018, Mw 7.1 Anchorage earthquakes) presented an opportunity to analyze these sequences in detail. This included the modelling of the frequency-magnitude statistics of the corresponding aftershock sequences. In addition, the aftershock occurrence rates were modelled using the Omori–Utsu (OU) law and the Epidemic Type Aftershock Sequence (ETAS) model. For each sequence, the calculation of the probability to have the largest expected aftershock during a given forecasting time interval was performed using both the extreme value theory and the Bayesian predictive framework. For the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution was performed to generate the chains of the model parameters. These MCMC chains were used to simulate the models forward in time to compute the predictive distributions. The calculation of the probabilities to have the largest expected aftershock to be above a certain magnitude after a mainshock using the Bayesian predictive framework fully takes into account the uncertainties of the model parameters. Moreover, in order to investigate the credibility of the obtained forecasts, several statistical tests were conducted to compare the performance of the earthquake rate models based on the OU formula and the ETAS model. The results indicate that the Bayesian approach combined with the ETAS model produced more robust results than the standard approach based on the extreme value distribution and the OU law.

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.

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.

Microseismicity is expected in potash mining due to the associated rock-mass response. This phenomenon is known, but not fully understood. To assess the safety and efficiency of mining operations, producers must quantitatively discern between normal and abnormal seismic activity. In this work, statistical aspects and clustering of microseismicity from a Saskatchewan, Canada, potash mine 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. To study the clustering aspects of the observed microseismicity, the nearest-neighbor distance (NND) method is applied. This allowed the identification of potential cluster characteristics in time, space, and magnitude domains. The implemented modeling approaches and obtained results will be used to further advance strategies and protocols for the safe and efficient operation of potash mines.