A new broad-band ocean bottom seismograph and characteristics of the seismic ambient noise on the South China Sea seafloor based on its recordings

The ocean is the primary source of seismic ambient noise. Therefore, seismic recordings at seafloor stations should reveal noise characteristics more directly than land stations. However, due to a lack of broad-band seismic instrumentation, seafloor noise studies using seafloor stations have been inadequate compared to land-based instrumentation. In this study, we use seismic data collected at the South China Sea (SCS) seafloor by newly developed ocean bottom seismographs (OBSs) to analyze the ambient noise features in this marginal sea. The broad-band OBS, dubbed 'Pankun', has unique shielding to isolate its sensor from the influences of bottom currents. A side-by-side land test between the OBS sensor unit and a standalone seismometer showed that the self-noise caused by the gimbal and the pressure case is insignificant. The recordings on the SCS seafloor have distinct noise spectra. The double frequency microseisms (DFMs) have a single instead of double peak like that seen for Pacific stations. The peak appears in a lower period range (1-5 s) than in the global noise model, indicating that the primary source region for the DFM is the SCS itself. The high-frequency content of the DFM is attenuated more as it propagates from its source region (seafloor) to land stations. The single frequency microseism (SFM) peak on the spectrum is weak, reflecting that SFMs, generated in shallow water along the coast, have difficulties propagating back into the deep ocean due to the substantial increase in seafloor depth. A long-period Earth's hum signal is also identifiable on the vertical component at periods greater than 50 s, probably due to the anti-current design of the OBS. Although the seasonal sea state mainly affects the noise level, extreme events such as typhoons can produce short-term abnormally high DFMs in the basin. However, the DFM highs caused by such events exhibit complex patterns, depending on the wind speed, duration, and area covered by the events.