Implications of the statistics of seismicity recorded within the Groningen gas field

Abstract

We reanalysed the induced seismicity data from the Groningen gas reservoir. We used the well-maintained induced event catalogue of the KNMI. The distributions of seismic moments and interevent times show a power law behaviour over several decades, and we find that upon increasing the magnitude threshold, these distributions remained scale-invariant. Because of this scale-invariance, we can put a constraint on the average loading of the elastic energy within the reservoir, which upon reaching a critical value gives rise to the seismic events. We find that the elastic energy roughly increases proportional to time. We also propose a new machine learning approach for declustering the seismic events, separating correlated from independent events. We find that only few events are truly independent, i.e. exponentially distributed over time. There are also few aftershocks following an Omori-type power law. The bulk of events presents a Gamma distribution for interevent times. This gives us an indication that the rigidity in the reservoir is high, but whether this results in overall correlated events should be settled with physics-based arguments rather than statistical ones.

Recent advances in earthquake seismology using machine learning

“…They employed the random forest model to classify activities overlapping in the spatiotemporal distance space. Trampert et al (2022) assumed the interevent-time distribution of induced earthquakes around gas reservoirs as a mixture of Omori, gamma, and exponential distributions. By applying gradient boosting regression, they concluded that the proportion of the Omori distribution (i.e., the fraction triggered by other earthquakes) was small.…”

4D Physics-Based Pore Pressure Monitoring Using Passive Image Interferometry

“…The Groningen region in the Netherlands has been studied extensively in the context of induced seismicity (e.g., Nepveu et al, 2016;Hettema et al, 2017;Bourne et al, 2018;Trampert et al, 2022) and subsidence (e.g., Van Thienen-Visser et al, 2015;Van der Wal & Van Eijs, 2016;Van Thienen-Visser & Fokker, 2017). The installation of a large dense network of borehole geophones (Dost et al, 2017) enabled intensive research activity.…”

4D Physics‐Based Pore Pressure Monitoring Using Passive Image Interferometry

“…The Groningen region in the Netherlands has been studied extensively in the context of induced seismicity (e.g., Bourne et al., 2018; Hettema et al., 2017; Nepveu et al., 2016; Trampert et al., 2022) and subsidence (e.g., Van der Wal & Van Eijs, 2016; Van Thienen‐Visser & Fokker, 2017; Van Thienen‐Visser et al., 2015). The installation of a large dense network of borehole geophones (Dost et al., 2017) enabled intensive research activity.…”