Assessment of S-net seafloor pressure data quality in view of seafloor geodesy

Author:

Hino Ryota1ORCID,Kubota Tatsuya2,Chikasada Naotaka Y2,Ohta Yusaku3,Otsuka Hideto3

Affiliation:

1. Tohoku University, Japan

2. National Research Institute for Earth Science and Disaster Resilience

3. Tohoku University

Abstract

Abstract Long-term continuous observation of seafloor pressure is effective for detecting seafloor vertical deformations that are associated with transient tectonic phenomena such as slow slip events. Since the aseismic slip event prior to the 2011 Tohoku earthquake, several discoveries have been made with respect to spontaneous slow slip events and various other types of slow earthquake along the Japan and Kuril Trenches. The Seafloor observation network for earthquakes and tsunamis along the Japan Trench (S-net) is expected to provide invaluable information on slow slip activities via geodetic signals that are detected by pressure observation. This study inspects the quality of the S-net pressure data in view of seafloor geodesy by comparison with records obtained by more than 100 autonomous ocean bottom pressure recorders (OBPRs) deployed along the Japan Trench. OBPRs have long been standard tools in seafloor geodesy and the data collected is considered benchmark in terms of quality. Most of the S-net stations show noise levels that are considerably higher than those of the OBPRs over periods of more than 2 d. A strong correlation between pressure and temperature is thought to account for the long-term noise. In this study, the temperature-dependent fluctuation component was estimated by prediction filtering, and removed from the original data, leading to a significant reduction in the noise level at many stations, which reached levels almost equivalent to those of OBPRs at 51 stations. Although no significant pressure changes have been identified as associated with the 2018 Boso SSE or repeated tremor bursts in the northern Japan Trench thus far, these stations can be considered sufficiently sensitive to detect slow slip events occurring nearby.

Publisher

Research Square Platform LLC

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