Characterizing induced seismic events in the Groningen gas field using an efficient Hamiltonian Monte Carlo sampler: a case study

In May 2019, an earthquake with a magnitude of 3.4 (local magnitude) hit the area of the Westerwijtwerd village in the province of Groningen, the Netherlands. The event is the result of the gas extraction in the Groningen gas field and is one of the largest events to date. To better understand the source characteristics of the event, we apply a probabilistic full-waveform inversion technique that we recently developed to the event's recordings. Specifically, we use a variant of the Hamiltonian Monte Carlo (HMC) algorithm. When sampling high-dimensional model spaces, HMC is proven to be more efficient than the generic Metropolis-Hasting algorithm. Compared to probabilistic inversions of tectonic events, two main challenges arise while applying the algorithm. First, the prior information of the event is usually incomplete and inaccurate. That is, the only available information is (an estimate of) the hypocenter and origin time. Second, the frequency content of the induced event's seismograms is higher than that of typical tectonic events. This implies a higher non-linearity, which in turn complicates the ability of a probabilistic inversion algorithm to sample the model spaces, particularly when considering the first challenge. Consequently, to address both challenges, first, we develop a procedure to estimate the necessary prior information and use it as input to the HMC variant. Second, we run our HMC algorithm iteratively to mitigate the non-linearity. Using the relatively detailed velocity model of the Groningen gas field, we eventually estimate ten posteriors of the source parameters. The latter being the hypocenter (three parameters), the moment tensor (six independent parameters), and the origin time.