2.1 S-net seafloor pressure observation
Although details of the S-net system have previously been described by Aoi et al (2020), we reiterate the part that is related to pressure observation here. S-net consists of 150 seafloor seismic and water pressure stations (Fig. 1). The network is divided into six submarine fiber networks; subsystems S-1 to S-5 along the landward slope of the trench, and S-6, which is deployed on the incoming plate outside the trenches. The observation equipment is packed into anti-pressure cylindrical housing that is separated into wet and dry sections. The pressure sensors are placed in the wet section, which is filled with fluid and is isolated from seawater by bellows and diaphragm, which are constructed using flexible materials that allow equivalency of internal and external pressure. The dry section contains devices such as seismic sensors, electronic signal amplification and processing units, communication equipment, and power supply. The instruments at each station belonging to one subsystem are connected serially via submarine cables, much like the repeaters used for commercial communication. Observation systems that use this type of geometry are known as inline systems, and a similar structure is used in many other cable-based submarine earthquake and tsunami observation systems in Japan (Kanazawa and Hasegawa, 1997; Hirata et al., 2002).
As the location in which the instruments are installed is an active trawl fishing area, instruments were installed beneath the seafloor in water at depths of up to 1500 m, which is the operational range for trawling. The instruments were buried using a plow-type burial machine, which is also used for laying commercial submarine cables, and covered by soft sediments just below the seafloor to a thickness of several tens of centimeters.
2.2 Characteristics of S-net data
The sensor, Paroscientific (8B7000-2-005 or 8B8000-2-005, Paroscientific, Inc.) provides continuous recording at a sampling frequency of 10 Hz of both pressure and temperature. To investigate the nature of the seafloor pressure data for the purpose of detecting crustal deformation, continuous data comprising 1-h samples for the period from the beginning of observation in May 2016 to June 2022 were generated and analyzed. Power spectra for all 150 stations were obtained, allowing us to attain an overall picture of the characteristics obtained by S-net. The spectra were obtained using a moving 4096 h time window that was shifted 24 h and the median value taken as the representative spectrum for each individual observation station. An example of an obtained power spectra is shown in Fig. S1a. Significant diversity was observed in the power spectra for the different stations (Fig. S1b); thus, a frequency distribution of the power spectrum was created to understand the characteristics of this diversity (Figs. S1c and 2). The results show that the S-net stations can be classified according to the characteristics of the pressure data in the frequency domain.
The power spectra show broad distribution at the lowest frequency. To observe the variation in power for the lower frequency components, root-mean-square (RMS) amplitudes of the pressure records were obtained by applying a low pass filter with a cut-off frequency of 1.39E-3 cph (cycles per hour) or a period of 30 d. The inherent sensor drift in the pressure sensors is a well-known cause of long-period variation (Watts and Kontoyiannis, 1990; Polster et al., 2009). Therefore, the RMS was calculated after the drift, expressed as exponential plus linear function of time, was subtracted from the filtered waveform.
Figure 3a shows the difference in the RMS for pressure data obtained at all S-net stations. Considering that 1 hPa of water pressure change is roughly equivalent to 1 cm of vertical seafloor motion, it would be difficult to detect crustal deformation at stations where the RMS is > 10 hPa at 10 cm. Figure 4a and 4b shows examples of pressure records with large RMS. The records from station 3–26 (Fig. 4a) show annual variations with large amplitudes that are highly correlated with changes in the temperature, while irregular fluctuations are observed in the records from 4–24 (Fig. 4b). These fluctuations are not correlated with temperature variation and are suspected to be due to mechanical instability of the instruments that are associated with pressure sensing. The S-net pressure data also suffered from other factors that may reduce long-term stability, such as offsets due to strong ground motions that are irrelevant to the static displacement resulting from fault motions (Kubota et al., 2020), suggesting that it is difficult to detect long-term fluctuations that exceed one month from the S-net pressure data.
Focusing on frequency bands that are higher than 0.001 cph or for periods of < 1000 h demonstrates two prominent peaks in the frequency distribution of the spectral shape (Fig. 3a). One peak lies around the lowest power level in the entire spectrum, and we classified stations with this peak as Group 1. The other peak is located at a power level of approximately a couple of hundred times higher than that observe in the results of Group 1. Stations with this peak comprise the majority of S-net stations and are classified as Group 2. A few other stations demonstrated much higher power than the average power observed in Group 2.
An example of the records from a station in Group 1 (4–22) is shown in Fig. 4c. The RMS for the long period is 4.5 hPa, which is one of the lowest among all stations. Although the record at station 3–26 (Fig. 4a) has a large, long-period RMS (110.2 hPa), its power spectrum is close to that of Group 1 at high frequencies. The power spectrum is quiet in the short-period band, but rapidly and abruptly increases in the long-period side.
Examples of waveforms obtained at a Group 2 station (4–11) and a station with much higher power (4–10) are shown in Fig. 4d and 4e, respectively. The pressure fluctuations obtained at these stations appear to have a clear correlation with temperature changes.
Long-period instability can be expected to be removed as linear trends for periods of less than one month or thereabouts (~ 720 h). If the level of fluctuation within this period is small, it may be possible to detect short-term transient signals such as those caused by short-term slow slip events, even at stations with large, long-period fluctuations such as station 3–26.
The power spectra of the S-net observations were compared with those obtained by OBPRs, which have proven sufficiently quiet to detect small tectonic signals in previous studies (Ito et al., 2013; Hino et al., 2014; Wallace et al, 2016). The OBPR records are expected to more accurately elucidate the actual pressure variation that is occurring on the seafloor because OBPRs sensors are directly exposed to seawater. The frequency distribution of the OBPR power spectrum using records obtained from 122 OBPRs, which have been deployed since 2008 by Tohoku University, is shown in Fig. S2. All deployed OBPRs were equipped with pressure sensors in the Paroscientific 8000 series, with specifications identical to those used in S-net. The time window for spectral estimation was set to 2048 h because of the short observation period over which OBPRs function. The shape of the power spectrum is stable with little variation although the OBPRs were deployed at various times and locations. Therefore, the median value of the OBPR power spectra can be regarded as the standard for seafloor pressure change in this area. Superimposing the median spectrum onto the frequency distribution of the S-net power spectra (Fig. 2a) indicates that almost all of the S-net data are much noisier than the average OBPR in all frequency bands, and this difference is interpreted to be due to noise that is inherent in the S-net data.
As an indication of the quality of the S-net data, the Logged Power Ratio (LPR) was calculated using:
$$LP{R_k}=\frac{1}{N}\sum\limits_{{i=1}}^{N} {\left( {{{\log }_{10}}P_{k}^{{{\text{S-net}}}}\left( {{f_i}} \right) - {{\log }_{10}}{P^{{\text{OBPR}}}}\left( {{f_i}} \right)} \right)}$$
1
Where LPRk is a measurement of the deviation in the pressure power at the k-th S-net station \(P_{k}^{{{\text{S-net}}}}\left( f \right)\)obtained from the median OBPR spectra \({P^{{\text{OBPR}}}}\left( f \right)\) by the LPR and averaged in a frequency range from 1.4E-3 to 2.1E-2 cph or from 24–720 h.
The LPRs of the data from the Group 1 stations are distributed over a range of < 1. Compared to the OBPR data, the pressure data obtained at these stations differ by a factor of 10 in terms of power, or by up to a factor of 3 in amplitude, and may thus be useful for the detection of transient tectonic signals. Figure 3b shows the LPRs for all the S-net stations, and it is apparent that several stations with small LPRs are located near zones with active tectonic tremor.