The evolution of fluid injection-induced seismicity, generally characterized through the number of events or their seismic moment, depends on, among other factors, the injected fluid volume. Migration of seismicity is observed during those sequences and might be caused by a range of mechanisms: fluid pressure diffusion, fluid-induced aseismic slip propagating along a stimulated fault, interactions between earthquakes. Recent theoretical and observational developments underline the important effect on seismicity migration of structural parameters, like fault criticality, or injection parameters, like flow rate or pressurization rate. Here, we analyze two well-studied injection-induced seismic sequences at the Soultz-sous-Fôret and Basel geothermal sites, and find that the evolution of the seismicity front distance primarily depends on the injected fluid volume. Based on a fracture mechanics model, we develop new equations relating seismicity migration to injected fluid volume and frictional and structural properties of the fault. We find that the propagation of a fluid-induced aseismic slip front along the stimulated fault, triggering seismicity, explains well the observations made on the two sequences. This model allows us to constrain parameters describing the seismicity front evolution and explains the diversity of migration patterns observed in injection-induced and natural earthquake swarms.

Numerical simulations of Sequences of Earthquakes and Aseismic Slip (SEAS) have rapidly progressed to address fundamental problems in fault mechanics and provide self-consistent, physics-based frameworks to interpret and predict geophysical observations across spatial and temporal scales. To advance SEAS simulations with rigor and reproducibility, we pursue community efforts to verify numerical codes in an expanding suite of benchmarks. Here we present code comparison results from a new set of benchmark problems BP6-QD-A/S/C that consider a single aseismic slip transient induced by changes in pore fluid pressure consistent with fluid injection and diffusion in fault models with different treatments of fault friction. Ten modeling groups participated in problems BP6-QD-A and BP6-QD-S considering rate-and-state fault models using the aging and slip law formulations for frictional state evolution, respectively, allowing us to explore these ingredients across multiple codes and better understand how various computational factors affect the simulated evolution of pore pressure and aseismic slip. Comparisons of problems using the aging versus slip law illustrate how models of aseismic slip can differ in the timing and amount of slip achieved with different treatments of fault friction given the same perturbations in pore fluid pressure. We achieve excellent quantitative agreement across participating codes, with further agreement being found by ensuring sufficiently fine time-stepping and consistent treatment of remote boundary conditions. Our benchmark efforts offer a community-based example to reveal sensitivities of numerical modeling results, which is essential for advancing multi-physics SEAS models to better understand and construct reliable predictive models of fault dynamics.

Anthropogenic fluid injections at depth induce seismicity which is generally organized as swarms, clustered in time and space, with moderate magnitudes. While some similarities between swarms have already been observed, whether they are driven by the same mechanism is still an open question. Pore fluid pressure or aseismic processes are often proposed to explain observations, while recent models suggest that seismicity is triggered by fluid-induced aseismic slip. Using 22 natural and anthropogenic swarms, we observe that duration, migration velocity and total moment scale similarly for all swarms. This confirms a common driving process for natural and induced swarms and highlights the ubiquity of aseismic slip. We propose a method to estimate the seismic-to-total moment ratio, which is then compared to a theoretical estimation that depends on the migration velocity, the effective stress drop and the slip velocity. Our findings lead to a generic explanation of swarms driving process.