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 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) surface waves, improving data quality at remote GSN sites could greatly improve ray‐path coverage, and facilitate more accurate and higher resolution models of deep Earth structure.

中文翻译：

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全球地震网络的瑞利波幅度不确定性和对全球断层扫描的潜在影响

全球地震台网 (GSN) 是一个多用途、全球分布的地震台网，供地震学家使用，用于表征地震和研究地球内部。网络中的大多数站点都有两个并置的宽带地震仪，这使网络运营商能够识别潜在的元数据和传感器问题。在这项研究中，我们通过比较来自 Mw 6 的垂直分量瑞利波的波形和并置传感器对之间的较大事件，研究了在 GSN 上测量表面波的准确性。我们计算传感器对波形之间的幅度偏差和相关系数。总的来说，我们对 2010 年 1 月 1 日至 2020 年 1 月 1 日发生的事件中的超过 670,000 个事件-站对进行了测量。我们发现平均传感器对振幅偏差，以及，因此，GSN 校准水平在 25-250 秒周期范围内约为 4%。尽管我们发现作为整个网络周期函数的传感器对幅度偏差几乎没有差异，但可用数据量随着周期的增加而迅速减少。例如，我们确定在 250 s 周期内仅超过 12% 的记录提供了可用的记录（例如，传感器对幅度偏差小于 20%，传感器对相关性大于 0.95）。然后，我们使用这些幅度估计偏差来确定数据覆盖范围和质量如何限制我们对整个地球 3D 衰减模型进行反演的能力。我们发现衰减模型的方差随着周期的增加而增加。例如，我们在 250 s 周期的 12 度衰减反演显示比我们在 25 秒的 12 度衰减模型多出 32% 的方差。这表明研究之间的深地幔断层扫描的差异可能是这些巨大不确定性的结果。由于这些高不确定性来自对长周期 (>100 s) 表面波的有限、高质量观测，提高远程 GSN 站点的数据质量可以大大提高射线路径覆盖率，并促进更准确和更高分辨率的模型深部地球结构。