2021
DOI: 10.1785/0220210094
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Why Do My Squiggles Look Funny? A Gallery of Compromised Seismic Signals

Abstract: Seismic instruments are highly sensitive and capable of recording a large range of different Earth signals. The high sensitivity of these instruments also makes them prone to various failures. Although many failures are very obvious, such as a dead channel, there are other more subtle failures that easily go unnoticed by both network operators and data users. This work documents several different types of failure modes in which the instrument is no longer faithfully recording ground-motion data. Although some … Show more

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Cited by 6 publications
(3 citation statements)
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“…Each estimate requires at least 90% data completeness within each 1-hour window. We zero-pad any data gaps within the 1-hour segments after linearly detrending each contiguous data segment to avoid broadband discontinuity-induced spectral artifacts 60 . For each subwindow we apply a Hann taper and calculate their average, removing the instrument response to obtain physical units as parameterized by authoritative EarthScope Data Management Center metadata, and returning acceleration PSD estimates in dB relative to 1 (ms −2 ) 2 /Hz.…”
Section: Methodsmentioning
confidence: 99%
“…Each estimate requires at least 90% data completeness within each 1-hour window. We zero-pad any data gaps within the 1-hour segments after linearly detrending each contiguous data segment to avoid broadband discontinuity-induced spectral artifacts 60 . For each subwindow we apply a Hann taper and calculate their average, removing the instrument response to obtain physical units as parameterized by authoritative EarthScope Data Management Center metadata, and returning acceleration PSD estimates in dB relative to 1 (ms −2 ) 2 /Hz.…”
Section: Methodsmentioning
confidence: 99%
“…Data used in the inversion were screened by excluding any data that were excessively noisy, such that the signal from the landslide was not visible when displacement waveforms were filtered to the frequency band of interest (period range of 15–250 s), or that had data gaps or other signal issues (e.g., Ringler et al., 2021). We did not impose a zero‐amplitude requirement on the data prior to the start time of the signal (which is sometimes done to remove precursory noise related to the band‐limited nature of the inversion, see Section 5.2) to allow for a more direct comparison with the filtered runout model results, discussed in the following section.…”
Section: Methodsmentioning
confidence: 99%
“…Each estimate requires at least 90% data completeness within the 1-hour window. We zero-pad any data gaps within the 1-hour segments after linearly detrending each contiguous data segment to avoid broadband discontinuity-induced spectral artifacts [73]. For each subwindow we apply a Hann taper and calculate their average, removing the instrument response to obtain physical units as parameterized by authoritative DMC metadata, and returning acceleration PSD estimates in dB relative to 1 (nm/s 2 ) 2 /Hz.…”
Section: Appendix B Spectral Estimationmentioning
confidence: 99%