Rayleigh wave amplitude uncertainty across the Global Seismographic Network and potential implications for global tomography

The Global Seismographic Network (GSN) is a multiuse, globally distributed seismic network used by seismologists, to both characterize earthquakes and study the Earth’s interior. Most stations in the network have two collocated broadband seismometers, which enable network operators to identify potential metadata and sensor issues. In this study, we investigate the accuracy with which surface waves can be measured across the GSN, by comparing waveforms of vertical‐component Rayleigh waves from Mw">MwMw 6 and larger events between collocated sensor pairs. We calculate both the amplitude deviation and correlation coefficient between waveforms at sensor pairs. In total, we make measurements on over 670,000 event–station pairs from events that occurred from 1 January 2010 to 1 January 2020. We find that the average sensor‐pair amplitude deviation, and, therefore, GSN calibration level, is, approximately, 4% in the 25–250 s period band. Although, we find little difference in sensor‐pair amplitude deviations as a function of period across the entire network, the amount of useable data decreases rapidly as a function of increasing period. For instance, we determined that just over 12% of records at 250 s period provided useable recordings (e.g., sensor‐pair amplitude deviations of less than 20% and sensor‐pair correlation greater than 0.95). We then use these amplitude‐estimate deviations to identify how data coverage and quality could be limiting our ability to invert for whole Earth 3D attenuation models. We find an increase in the variance of our attenuation models with increasing period. For example, our degree 12 attenuation inversion at 250 s period shows 32% more variance than our degree 12 attenuation model at 25 s. This indicates that discrepancies of deep‐mantle tomography between studies could be the result of these large uncertainties. Because these high uncertainties arise from limited, high‐quality observations of long‐period (⁠>100  s">