Characteristics of Slow Slip Event in March 2020 Revealed From Borehole and DONET Observatories

Abstract

We have detected an event of pore pressure change (hereafter, we refer it to “pore pressure event”) from borehole stations in real time in March 2020, owing to the network developed by connecting three borehole stations to the Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) observatories near the Nankai Trough. During the pore pressure event, shallow very low-frequency events (sVLFEs) were also detected from the broadband seismometers of DONET, which suggests that the sVLFE migrated toward updip region along the subduction plate boundary. Since one of the pore pressure sensors have been suffered from unrecognized noise after the replacement of sensors due to the connecting operation, we assume four cases for crustal deformation component of the pore pressure change. Comparing the four possible cases for crustal deformation component of the volumetric strain change at C0010 with the observed sVLFE migration and the characteristic of previous SSEs, we conclude that the pore pressure event can be explained from SSE migration toward the updip region which triggered sVLFE in the passage. This feature is similar to the previous SSE in 2015 and could be distinguished from the unrecognized noise on the basis of t-test. Our new finding is that the SSE in 2020 did not reach very shallow part of the plate interface because the pore pressure changes at a borehole station installed in 2018 close to the trough axis was not significant. In the present study, we estimated the amount, onset and termination time of the pore pressure change for the SSE in 2020 by fitting regression lines for the time history. Since the change amount and duration time were smaller and shorter than the SSE in 2015, respectively, we also conclude that the SSE in 2020 had smaller magnitude that the SSE in 2015. These results would give us a clue to monitor crustal deformation along the Nankai Trough directly from other seafloor observations.