Japan Geoscience Union Meeting 2024

Presentation information

[J] Poster

S (Solid Earth Sciences ) » S-GD Geodesy

[S-GD02] Crustal Deformation

Fri. May 31, 2024 5:15 PM - 6:45 PM Poster Hall (Exhibition Hall 6, Makuhari Messe)

convener:Fumiaki Tomita(International Research Institute of Disaster Science, Tohoku University), Masayuki Kano(Graduate school of science, Tohoku University), Akemi Noda(Meteorological Research Institute, Japan Meteorological Agency), Yuji Himematsu(Geospatial Information Authority of Japan)

5:15 PM - 6:45 PM

[SGD02-P03] Attempting to detect intermediate-term slow slip events in the shallow region of the Suruga Trough

*Yuji Kikuchi1, Yuta Mitsui1, Shogo Ishizuka2, Masayuki Kano3 (1.Shizuoka University, 2.University of Tsukuba, 3.Tohoku University)

Keywords:Intermediate-term slow slip events, Suruga trough, Baseline change

Many short-term slow slip events (S-SSEs) that last from 1 to 10 days have been detected in the Nankai Trough subduction zone, Japan, using the Global Navigation Satellite System (GNSS), strainmeters, and tiltmeters (e.g., Okada et al., 2022; Yabe et al., 2023). Recently, SSEs with durations significantly longer than those of typical S-SSEs have been observed near the trench, especially off the Kii Peninsula and Cape Muroto (e.g., Yokota and Ishikawa, 2020).

In our study, we estimated the time periods when intermediate-term SSEs may have occurred in the shallow part of the Suruga Trough by observing changes in the GNSS baseline lengths between the upper and lower plates of the trough. In the first methodology, we calculated the correlation coefficients between the GNSS baseline length data and simulated templates of surface displacement associated with SSEs, and conducted peak extraction by converting the moving average of the baseline length data into velocity. Both processes aimed to identify periods harmonious with SSEs. Within the analysis period, some candidate SSE signals were detected. As the second approach, we employed the L1 trend filter (Yano and Kano, 2022) to represent the baseline length data as sparse piecewise linear functions and sought combinations where the second derivative transitions from negative to positive. This method led to the detection of another SSE signal candidate.

For further analysis, we used elastic Green's function to make a preliminary fault model, utilizing the observed displacements during the possible SSE periods. Additionally, we analyzed multi-component strainmeter data from the Japan Meteorological Agency to confirm whether strain changes corresponded with the strainmeter data during the potential SSE periods.