Japan Geoscience Union Meeting 2024

Presentation information

[E] Poster

S (Solid Earth Sciences ) » S-SS Seismology

[S-SS03] Seismological advances in the ocean

Mon. May 27, 2024 5:15 PM - 6:45 PM Poster Hall (Exhibition Hall 6, Makuhari Messe)

convener:Lina Yamaya(National Research Institute for Earth Science and Disaster Resilience), Takashi Tonegawa(Research and Development center for Earthquake and Tsunami, Japan Agency for Marine-Earth Science and Technology), Tatsuya Kubota(National Research Institute for Earth Science and Disaster Resilience)

5:15 PM - 6:45 PM

[SSS03-P07] Noise on Ocean Bottom Seismometers: Observations and New Directions

*Helen A Janiszewski1, Joshua Russell2, Charles Hoots1, Elliona Maso1 (1.University of Hawaii at Manoa, 2.Syracuse University)

Keywords:ocean bottom seismology, ambient noise, marine geophysics

The proliferation of broadband ocean bottom seismometer (BBOBS) deployments has generated key datasets from diverse marine environments, improving our understanding of otherwise inaccessible ocean basin structure and evolution as well as tectonic and earthquake processes occurring at the plate boundaries. Recent development of community software has made these datasets more accessible and, in turn, the community of scientists using this data has expanded. This growth in BBOBS data collection is likely to persist with the arrival of new seismic seafloor technologies and continued scientific interest in marine and amphibious (shoreline crossing) targets. However, the noise inherent in BBOBS data poses a challenge that is markedly different from that of terrestrial data. Sources of noise on the seafloor, the degree to which they couple to the seismometer housing, and their variation with seafloor environment are often not well understood. Here, we compute global trends in BBOBS noise (e.g., impact of water depth and seismometer type on compliance and tilt noise), and use these observations to motivate more detailed investigations of novel instrumentation and methodologies for noise characterization and removal. This includes the utility of denoising techniques for improving ambient-noise-based imaging, comparisons of temporary broadband OBS data with permanent cabled instrumentation, and exploration of non-traditional (e.g. machine learning based) noise removal strategies.