Estimating induced seismic source parameters using an efficient Hamiltonian Monte Carlo algorithm

Abstract

Induced seismicity, referring to earthquakes triggered by human activities, has become a pressing concern, particularly in regions with extensive subsurface resource extraction, such as the Groningen gas field in the Netherlands. Decades of natural gas production have altered regional subsurface stress and pressure, reactivating faults and leading to frequent earthquakes, which pose significant structural and societal concerns. To better understand the nucleation and mechanisms behind these earthquakes, source characterization has become a fundamental scientific approach. Accurate characterization not only provides insights into earthquake dynamics but also plays a crucial role in developing subsurface models, optimizing extraction techniques, and helping to mitigate future seismic risks. This research focuses on designing an efficient characterization method by developing a probabilistic workflow for estimating induced earthquake source parameters. Specifically, it integrates advanced probabilistic inversion techniques with geological knowledge to enhance accuracy while reducing computational costs. The proposed workflow leverages a variant of the Hamiltonian Monte Carlo (HMC) algorithms, which offers advantages over a traditional Markov Chain Monte Carlo (MCMC) algorithm, particularly in handling high-dimensional parameter spaces. The workflow estimates key earthquake source parameters, including the centroid, moment tensor, and origin time, even when prior information is limited or uncertain. Initial validation through a synthetic earthquake scenario demonstrates the workflow’s ability to recover source characteristics with high confidence. To further evaluate its validity, the workflow is first tested on a geomechanically simulated synthetic event before being applied to real earthquake data from Groningen. A geomechanical simulation of the 2018 M$_L$ 3.4 Zeerijp event evaluates whether a point source assumption sufficiently represents induced earthquakes compared to finite fault models. The results indicate that, despite simplifications, estimated earthquake parameters remain robust, suggesting that computationally expensive finite fault models may not always be necessary. Building on this, the workflow is then applied to real earthquake data from the 2019 M$_L$ 3.4 event below Westerwijtwerd village, where geological prior knowledge is incorporated to enhance computational efficiency. Using fault-based prior distributions instead of uniform priors significantly reduces computational time while maintaining accuracy, demonstrating the benefits of integrating geological constraints into probabilistic inversion. Expanding its applicability, the workflow is further employed to characterize ten additional earthquakes (M$_L > 2$) in Groningen, where it is combined with rupture directivity analysis to provide insights into rupture dynamics. The results show that normal faulting is the dominant mechanism, aligning with regional geological structures. While rupture directivity effects are minor, they exhibit small correlations with known fault orientations. The study concludes with recommendations for future improvements, such as incorporating S-wave analysis, which could enhance the accuracy of the estimated parameters. Additionally, a more rigorous treatment of uncertainty in both observed and synthetic seismograms is suggested to improve probabilistic estimates. All in all, this research contributes to the advancement of seismic inversion techniques, offering valuable tools for assessing and mitigating the risks associated with induced seismicity. The proposed workflow provides a framework applicable beyond Groningen, aiding in the characterization of induced earthquakes in other regions affected by subsurface activities.