The systematic determination of the source characteristics of global earthquakes and other seismic sources in a robust and consistent manner is a paramount task in seismology. The Global Centroid Moment Tensor (GCMT) project (Ekström et al., 2012), employing an elegant inversion approach and thoughtful data selection, has been a standard bearer for such an autonomous earthquake catalog. This, by no means an all-encompassing review, celebrates the long-lasting impact and legacy it has left on the seismological and broader Earth science community, from tectonics and structural geology to geodesy and hazard assessment. We also identify and discuss three areas that, in our view, are subject to potential improvement in the current GCMT practice. These include (i) enhanced quantification of uncertainty in MT solutions, (ii) utilization of 3D Earth models, and (iii) robust development of dynamic models that extend beyond a point source assumption in time and space. Recent developments in various areas of theoretical and observational seismology, such as advances in Bayesian inversion, 3D waveform modeling, and applied machine learning methods, will enable the integration of these needed elements into the next generation of routine earthquake catalogs.
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