Removing Noise from the Vertical Component Records of Ocean‐Bottom Seismometers: Results from Year One of the Cascadia Initiative

TLDR: In this paper, the authors developed a methodology for reducing the amplitude of vertical noise sources by 1-2 orders of magnitude, revealing many events that could not be distinguished before noise reduction, and correcting for any signal distortion caused by the noise removal.

Abstract: Through an array of ocean‐bottom seismometers, the Cascadia Initiative is investigating the structure of the Juan de Fuca and Gorda plates. Because the instruments are on the seafloor, they are subject to substantial noise from water waves and bottom currents, especially at long periods. If the seismometer is slightly tilted, some of the high bottom current noise on the horizontals leaks onto the vertical record. Another major type of noise, compliance noise, is created when pressure variations associated with infragravity waves physically deflect the seabed. Extending the work of Crawford and Webb (2000), we developed a methodology for reducing the amplitude of vertical noise sources by 1–2 orders of magnitude, revealing many events that could not be distinguished before noise reduction, and for correcting for any signal distortion caused by the noise removal. We use the horizontal records to predict and remove the tilt noise from the verticals, and we use the pressure records to predict and remove the compliance noise from the first year of Cascadia Initiative data. After determining the degree and direction of tilt for each day at each station, we assessed the success of different instrument designs at minimizing tilt noise; external shielding of the seismometer sensor package is effective at reducing bottom current noise. Tilt at individual instruments can vary continuously throughout a several month deployment and should be determined at least daily for optimal noise removal. By understanding and reducing these noise sources, we hope to open the Cascadia dataset to a wider range of applications.