Deterministic earthquake prediction remains elusive, but time-dependent probabilistic seismicity forecasting seems within reach thanks to the development of physics-based models relating seismicity to stress changes. Difficulties include constraining the earthquake nucleation model and fault initial stress state. Here, we analyze induced earthquakes from the Groningen gas field, where production is strongly seasonal, and seismicity began 3 decades after production started. We use the seismicity response to stress variations to constrain the earthquake nucleation process and calibrate models for time-dependent forecasting of induced earthquakes. Remarkable agreements of modelled and observed seismicity are obtained when we consider (i) the initial strength excess, (ii) the finite duration of earthquake nucleation, and (iii) the seasonal variations of gas production. We propose a novel metrics to quantify the nucleation model’s ability to capture the damped amplitude and the phase of the seismicity response to short-timescale (seasonal) stress variations which allows further tightening the model’s parameters.

Reservoir operations related to natural gas extraction, fluid disposal, carbon diox-ide storage, or geothermal energy production, are capable of inducing seismicity. Mod-eling tools have been developed that allow for quantitative forecasting of seismicity basedon operations data, but the computational cost of such models and the difficulty in rep-resenting various sources of uncertainties make uncertainty quantification challenging.We address this issue in the context of an integrated modeling framework, which com-bines reservoir modeling, geomechanical modeling, and stress-based earthquake forecast-ing. We use the Groningen gas field as a case example of application. The modeling frame-work is computationally efficient thanks to a 2-D finite-element reservoir model whichassumes vertical flow equilibrium, and the use of semi-analytical solutions to calculateporoelastic stress changes and predict seismicity rate. The earthquake nucleation modelis based on rate-and-state friction and allows for an initial strength excess so that thefaults are not assumed initially critically stressed. The model parameters and their un-certainties are estimated using either a Poisson or a Gaussian likelihood. We investigatethe effect of the likelihood choice on the forecast performance and we estimate uncer-tainties in the predicted number of earthquakes as well as in the expected magnitudes.We use a synthetic catalog to estimate the improved forecasting performance that wouldhave resulted from a better seismicity detection threshold. Finally, we use tapered andnon-tapered Gutenberg-Richter distributions to evaluate the most probable maximummagnitude over time and account for uncertainties in its estimation. We show that theframework yields realistic estimates of the seismicity model uncertainties and is appli-cable for operational forecasting or to design induced seismicity monitoring. It could alsoserve as a basis for probabilistic traffic-light systems.