DI016-0004
Rayleigh Wave Amplitude Uncertainty Across the Global Seismographic Network and Potential Implications for Global Tomography
Rayleigh Wave Amplitude Uncertainty Across the Global Seismographic Network and Potential Implications for Global Tomography
Friday, 11 December 2020
Poster
Abstract:
The Global Seismographic Network (GSN) is a multiuse, globally distributed seismic network used by scientists to characterize earthquakes and study the Earth’s interior. Nearly all stations in the network have two, co-located broadband seismometers, which enables network operators to identify potential metadata discrepancies and sensors issues. In this study, we investigate the accuracy to which surface waves can be measured across the GSN. We achieve this by comparing amplitudes of vertical component Rayleigh waves from M6 and larger events between co-located sensor pairs. In total, we make over 670,000 station event-pair measurements from January 1, 2010 to January 1, 2020 and find that the GSN is calibrated to approximately 4% from 25 s period to 250 s. While we find little difference in the relative 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., co-located relative deviations of less than 20% and correlation between signals greater than 0.95). We then use these amplitudes to estimate deviations in order to identify in what way station coverage and data quality could be limiting our ability to invert for whole Earth 3D attenuation models. We find an increase in the variance of our models with increasing period. For example, our degree 12 attenuation inversion at 250 s period shows 32% more variance than our degree 12 attenuation inversion at 25 s. This suggests that some discrepancies between deep mantle attenuation tomography models could be the result of these large uncertainties in GSN data. Because these large uncertainties arise from limited, high-quality observations of long-period (>100 s) surface waves, improving data quality at remote GSN sites could greatly improve ray-path coverage and facilitate constraining models of deep earth structure.

