Statistical Modeling and Characterization of Induced Seismicity within the Western Canada Sedimentary Basin

Kothari, Sidhanth; Shcherbakov, R.; Atkinson, Gail M.

doi:10.1002/essoar.10503731.1

Cited by 5 publications

(3 citation statements)

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“…Several cluster characteristics are also provided. These include: the number of events in each cluster N , the cluster duration in days T d , the difference between the largest (root) and the second largest event ∆m, the average leaf depth ⟨d⟩, the normalized leaf depth δ = ⟨d⟩ √ N , and the inverted branching number B I (Zaliapin & Ben-Zion, 2016;Kothari et al, 2020).…”

Section: Distribution and Structure Of The Event Clustersmentioning

confidence: 99%

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Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Sedghizadeh

Berghe

Shcherbakov

2022

Preprint

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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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Section: Distribution and Structure Of The Event Clustersmentioning

confidence: 99%

“…The NND method was also applied to analyse the seismicity in the Western Canada Sed-imentary Basin due to significant increase in induced seismicity observed in the last decade (Maghsoudi et al, 2016(Maghsoudi et al, , 2018Kothari et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Sedghizadeh

Berghe

Shcherbakov

2022

Preprint

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“…It was also adapted to perform declustering of earthquake catalogs [50]. The NND method was also applied to analyze the seismicity in the Western Canada Sedimentary Basin due to a significant increase in induced seismicity observed in the last decade [51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Sedghizadeh

Berghe²,

Shcherbakov

2023

Front. Appl. Math. Stat.

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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.

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Nearest-Neighbour Distance Analysis of Induced and Natural Seismicity within the Western Canada Sedimentary Basin

Kothari

Shcherbakov

Atkinson

2021

Preprint

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Statistical Modeling and Characterization of Induced Seismicity within the Western Canada Sedimentary Basin

Cited by 5 publications

References 0 publications

Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Statistical and clustering analysis of microseismicity from a Saskatchewan potash mine

Nearest-Neighbour Distance Analysis of Induced and Natural Seismicity within the Western Canada Sedimentary Basin

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