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 effciency 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 effcient operation of potash mines.  

The 2019 Ridgecrest, California, earthquake sequence represents a complex pattern of seismicity that is characterized by the occurrence of a well defined foreshock sequence followed by a mainshock and subsequent aftershocks. In this work, a detailed statistical analysis of the sequence is performed. Particularly, the parametric modelling of the frequency-magnitude statistics and the earthquake occurrence rate is carried out. It is shown that the clustering of earthquakes plays an important role during the evolution of this sequence. In addition, the problem of constraining the magnitude of the largest expected aftershocks to occur during the evolution of the sequence is addressed. In order to do this, two approaches are considered. The first one is based on the extreme value theory, whereas the second one uses the Bayesian predictive framework. The latter approach has allowed to incorporate the complex earthquake clustering through the Epidemic Type Aftershock Sequence (ETAS) process and the uncertainties associated with the model parameters into the computation of the corresponding probabilities. The results indicate that the inclusion of the foreshock sequence into the analysis produces higher probabilities for the occurrence of the largest expected aftershocks after the M7.1 mainshock compared to the approach based on the extreme value distribution combined with the Omori-Utsu formula for the earthquake rate. Several statistical tests are applied to verify the forecast.

In western Canada, there has been an increase in seismic activity linked to anthropogenic energy-related operations including conventional hydrocarbon production, wastewater fluid injection, and, more recently, hydraulic fracturing (HF). Statistical modeling and characterization of the space, time, and magnitude distributions of the seismicity clusters is vital for a better understanding of induced earthquake processes and development of forecasting models. In this work, a statistical analysis of the seismicity in the Western Canada Sedimentary Basin was performed across past and present time periods by utilizing a compiled earthquake catalogue for Alberta and eastern British Columbia. Specifically, the inter-event space-time distance distributions of earthquakes were studied using the nearest-neighbour distance (NND) method. Additionally, the frequency-magnitude statistics and aftershock parameters of several clusters were analyzed using the Gutenberg-Richter relation and the epidemic type aftershock sequence model. The results suggest that recent regional changes in the NND distributions, namely, a disproportionate increase in loosely and tightly clustered seismic activity over time, are unnatural and likely due to the rise in HF operations for the development of unconventional resources. It is concluded that both these loosely and tightly clustered earthquake subpopulations differ measurably from what may be the region’s tectonic seismic activity. Additionally, HF treatments have a greater probability of triggering swarm-like sequences that sharply spike the seismicity rate and are characterized by larger Gutenberg-Richter b-values. In contrast, conventional production and wastewater disposal operations largely trigger loosely clustered activity with more typical magnitude-occurrence distributions.

Over the past decade, parts of western Canada have seen a rise in clustered seismic activity coinciding with the growing use of a hydrocarbon reservoir stimulation technique known as hydraulic fracturing. This recent upsurge has the potential to increase the local seismic hazard, particularly in affected areas characterized by a sparser tectonic environment. It is therefore critically important to assess and characterize the space, time and magnitude distributions of induced earthquakes from a statistical standpoint, in order to develop a better understanding of triggering processes and improve forecasting models. In this study, the nearest-neighbour distance method was used to analyze the distribution of space-time inter-event distances across the Western Canada Sedimentary Basin from a regional perspective. Additionally, the epidemic type aftershock sequence model and the Gutenberg-Richter relation were used to compare the structuring and magnitude scaling of several seismic clusters induced by different human operations. The results demonstrate that a transformation in the regional distribution of inter-earthquake distances occurred after 2009, where an emergent subpopulation of abnormally tightly clustered events became distinguishable from both natural and prior-induced seismicity. Several distinctions were also revealed between earthquake clusters occurring near different anthropogenic operations, including a higher proportion of tightly clustered events near hydraulic fracturing treatments which were largely swarm-like in nature.