During the three-year duration, we first secured and collated multiple seismicity datasets (>30 datasets), including natural seismicity due to tectonic and volcanic activity and induced seismicity associated with EGS, WWD, CCS and HF, thus providing a range of behaviour and geological settings. Next, we analyzed the datasets, testing multiple advanced approaches, such as automatic clustering, events filtering, point cloud mapping (e.g. Fig. 1), M-T and R-T diagrams, and b-values analysis. In particular, we developed a new approach analyzing swarm's spatio-temporal development based on detailed mapping of isochrone contours on reactivated fault surface (e.g. Fig. 1) and measured fundamental parameters such as step size and maximum magnitude to investigate the conditions for step blocking or transferring deformation quantitatively. Finally, we developed a numerical modelling approach to incorporate realistic fault geometry into IS magnitude prediction. The chosen method relies on a stochastic approach based on quantitative fault zone parameterization and permits the simulation of rupture dynamics based on structural controls. Finally, this method has been tested and validated through best-in-class case studies (i.e. Harrison County, Ohio; Yushu-Ganzi, central-eastern Tibet; and NAF fault, Türkiye) and a sensitivity analysis.