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OBP data processing for measurement of seafloor deformation
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Ocean-bottom pressure data are affected by ocean mass variations, so reliable seafloor level (SFL)
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estimations cannot be obtained without compensating for those variations. Ocean mass variations caused by
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ocean tides were estimated by harmonic analyses using the BAYTAP-G model of Tamura et al. [1991] and
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removed from the original OBP records. The motion of the seawater layer, another major component of
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ocean mass variation, was estimated using a global barotropic ocean model forced by synoptic atmospheric
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disturbances [Inazu et al., 2012], and its contribution to recorded pressures was subtracted from the
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tide-free pressure data. See Inazu et al. [2012] for more detailed description of the ocean mass variation
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correction. Long-term pressure recordings often suffer from instrumental drift that is characteristic of
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quartz pressure sensors [e.g., Watts and Kontoyiannis, 1990]. Here, we assumed that drift could be
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expressed as a linear function of time. We estimated the rate of temporal change in observed pressure data
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from 1 January to 8 March and subtracted the linear drift component from the data. After removing the
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linear trend, we converted the pressure data to SFL by using a constant seawater density of 1.030 × 103
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kg/m3.
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Co- and postseismic seafloor deformations due to the M7.3 foreshock were measured according to a
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time series of differential SFL (DSFL) between pairs of stations. Even after correcting for variations of
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ocean mass variation, the SFL data contained short-period (<1 day) fluctuations. Because fluctuations in the
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OBP data may be regarded as noise that is irrelevant to crustal deformation, and because they were similar
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among stations, we effectively cancelled the effect of noise by using the differences between the SFL
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records of pairs of stations [Fujimoto et al., 2003].
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We regarded the SFL records at station P08, located in the middle of the OBP array, as a common
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reference for obtaining the DSFL time series. The SFL data recorded at this station were relatively stable
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until the occurrence of the Mw9.0 mainshock. Thus, we assumed that the SFL fluctuations at station P08
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were representative of the noise of all stations.
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At station P09, the coseismic step due to another large aftershock (Mw6.5, at 21:24 on 9 March) was
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observed. We assumed that the effect of this earthquake was too small to be detected by other OBPs and
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removed the step from the SFL record at P09 (0.042 m) to estimate the deformation caused by the afterslip
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of the M7.3 earthquake recorded by the rest of the OBP network.
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We fitted a linear function to the DSFL time series to estimate the coseismic steps and the rate of
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postseismic deformation. The amount of postseismic deformation was obtained as the product of the rate of
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deformation and its duration.
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References
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Fujimoto, H., M. Mochizuki, K. Mitsuzawa, T. Tamaki, and T. Sato (2003), Ocean bottom pressure
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variations in the southern Pacific following the 1997-98 El Niño event, Geophys. Res. Lett., 30, 1456,
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doi:10.1029/2002GL016677.
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Inazu, D., R. Hino, and H. Fujimoto (2012), A global barotropic ocean model driven by synoptic
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atmospheric disturbances for detecting seafloor vertical displacements from in situ ocean bottom
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pressure measurements, Marine Geophys. Res., 33(2), 127-148, doi:10.1007/s11001-012-9151-7.
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Tamura, Y., T. Sato, M. Ooe, and M. Ishiguro (1991), A procedure for tidal analysis with a Bayesian
information criterion, Geophys. J. Int., 104, 507-516, doi:10.1111/j.1365-246X.1991.tb05697.x.
Watts D. R. and H. Kontoyiannis (1990), Deep-ocean bottom pressure measurements: Drift removal and
performance, J. Atmos. Oceanic Technol., 7, 296–306.