Early Tsunami Detection With Near‐Fault Ocean‐Bottom Pressure Gauge Records Based on the Comparison With Seismic Data

Abstract

Offshore real‐time ocean bottom networks of seismometers and ocean bottom pressure (OBP) gauges have been recently established such as DONET and S‐net around the Japanese islands. One of their purposes is to practice rapid and accurate tsunami forecasting. Near‐fault OBP records, however, are always contaminated by nontsunami components such as sea‐bottom acceleration change until an earthquake stops its fault or sea‐floor motions. This study proposes a new method to separate tsunami and ocean bottom displacement components from coseismic OBP records in a real‐time basis. Associated with the Off‐Mie earthquake of 2016 April 1, we first compared OBP data with acceleration, velocity, and displacement seismograms recorded by seismometers at common ocean bottom sites in both time and frequency domains. Based on this comparison, we adopted a band‐pass filter of 0.05–0.15 Hz to remove ocean‐bottom acceleration components from the OBP data. Resulting OBP waveforms agree well with the tsunami components estimated by a 100‐s low‐pass filter with records of several hundred seconds in length. Our method requires only an early portion of a given OBP record after 30 s of an origin time in order to estimate its tsunami component accurately. Our method enhances early tsunami detections with near‐fault OBP data; that is, it will make a tsunami forecasting system faster and more reliable than the previous detection schemes that require data away from source regions or after coseismic motions are over.

Extracting Near‐Field Seismograms From Ocean‐Bottom Pressure Gauge Inside the Focal Area: Application to the 2011 Mw 9.1 Tohoku‐Oki Earthquake

“…Based on the three‐dimensional theory, the pressure change inside the focal area includes a component related to vertically accelerating seafloor (Saito, 2013, 2017, 2019; An et al., 2017; Filloux, 1982). This component was confirmed by the actual observations and numerical simulations (e.g., An et al., 2017; Filloux, 1982; Ito et al., 2020; Kubota et al., 2020; Kubota, Saito, et al., 2017; Matsumoto et al., 2012, 2017; Mizutani et al., 2020; Nosov & Kolesov, 2007; Saito, 2019; Saito & Kubota, 2020; Saito & Tsushima, 2016; Webb, 1998). Additionally, the pressure change field includes the high‐frequency ocean‐acoustic waves related to the seawater elasticity (Lotto et al., 2019; Nosov & Kolesov, 2007; Saito & Tsushima, 2016).…”

Review on Recent Progress in Near-Field Tsunami Forecasting Using Offshore Tsunami Measurements: Source Inversion and Data Assimilation

“…As a straightforward approach, Kubota et al (2021b) developed a method to separate the dynamic pressure effect due to the ground motion from the OBPG data by using inversion analysis along with tsunami generation theory. Furthermore, Mizutani et al (2020) proposed a method to eliminate the effects of seismic and acoustic waves from OBPG records. They first compared the OBPG data with acceleration, velocity, and displacement seismograms recorded at the same location.…”

Improvement of near-field tsunami forecasting method using ocean-bottom pressure sensor network (S-net)

“…Saito and Tsushima (2016) showed that the first ~ 90 s of ocean-bottom pressure data is highly affected by acoustic waves and seismic waves generated by the earthquake. Mizutani et al (2020) recently developed an early tsunami detection method for near-fault ocean-bottom pressure data by comparing the seismic data and pressure data at the same site to eliminate nontsunami components. This tsunami detection method should be applied to original pressure data before our improved assimilation method is applied to the data.…”