scholarly journals Repetitive long-term slow slip events beneath the Bungo Channel, southwestern Japan, identified from leveling and sea level data from 1979 to 2008

2011 ◽  
Vol 116 (B4) ◽  
Author(s):  
Akio Kobayashi ◽  
Takeyasu Yamamoto
Keyword(s):  
Sea Level ◽  
Slow Slip ◽  
Slow Slip Events ◽  
Level Data ◽  
Bungo Channel ◽  
And Sea Level

scholarly journals Spatiotemporal Slip Distributions Associated with the 2018-2019 Bungo Channel Long-Term Slow Slip Event Inverted from GNSS Data

2021 ◽  
Author(s):  
Yukinari Seshimo ◽  
Shoichi Yoshioka
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Total Duration ◽  
Series Data ◽  
Slow Slip ◽  
Slow Slip Events ◽  
Bungo Channel ◽  
Slip Event ◽  
Gnss Time Series

Abstract Long-term slow slip events (L-SSEs) have occurred beneath the Bungo Channel with durations of several months to a couple of years repeatedly with a recurrence interval of approximately six years. We estimated the spatiotemporal slip distributions of the 2018–2019 Bungo Channel L-SSE inverted from processed GNSS time series data. This event was divided into two subevents, with the first on the southwest side of the Bungo Channel from 2018.3 to 2018.7 and the second beneath the Bungo Channel from 2018.8 to 2019.4. Tectonic tremors became active on the downdip side of the L-SSE occurrence region when large slow slips took place beneath the Bungo Channel. Compared with the previous Bungo Channel L-SSEs, this spatiotemporal slip pattern and amount were similar to those of the 2003 L-SSE. However, the slip expanded in the northeast-southwest direction in the latter half of the second subevent. We also found that the total duration of the two subevents was 1.0 year, which was the shortest among the four recent L-SSEs beneath the Bungo Channel identified using GNSS time series data. The maximum amount of slip, the maximum slip velocity, the total released seismic moment, and the moment magnitude of the 2018–2019 L-SSE were estimated to be 27 cm, 53 cm/year, 4.1×1019 Nm, and 7.0, respectively, all of which were the largest among the four L-SSEs.

scholarly journals Spatiotemporal slip distributions associated with the 2018–2019 Bungo Channel long-term slow slip event inverted from GNSS data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Yukinari Seshimo ◽  
Shoichi Yoshioka
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Slow Slip ◽  
Slow Slip Events ◽  
Bungo Channel ◽  
Slip Event ◽  
The Moment ◽  
Gnss Data ◽  
Gnss Time Series

AbstractLong-term slow slip events (L-SSEs) have repeatedly occurred beneath the Bungo Channel in southwestern Japan with durations of several months to a couple of years, with a recurrence interval of approximately 6 years. We estimated the spatiotemporal slip distributions of the 2018–2019 Bungo Channel L-SSE by inverting processed GNSS time series data. This event was divided into two subevents, with the first on the southwest side of the Bungo Channel from 2018.3 to 2018.7 and the second beneath the Bungo Channel from 2018.8 to 2019.4. Tectonic tremors became active on the downdip side of the L-SSE occurrence region when large slow slips took place beneath the Bungo Channel. Compared with the previous Bungo Channel L-SSEs, this spatiotemporal slip pattern and amount were similar to those of the 2002–2004 L-SSE. However, the slip expanded in the northeast and southwest directions in the latter half of the second subevent. The maximum amount of slip, the maximum slip velocity, the total released seismic moment, and the moment magnitude of the 2018–2019 L-SSE were estimated to be 28 cm, 54 cm/year, $$4.4 \times 10^{19}$$ 4.4 × 10 19 Nm, and 7.0, respectively, all of which were the largest among the 1996–1998, 2002–2004, 2009–2011, and 2018–2019 L-SSEs.

Estimation of frictional properties and slip evolution on a long-term slow slip event fault with the ensemble Kalman filter: numerical experiments

2019 ◽  
Vol 219 (3) ◽  
pp. 2074-2096 ◽  
Author(s):  
Kazuro Hirahara ◽  
Kento Nishikiori
Keyword(s):  
Surface Displacement ◽  
Slip Rate ◽  
Slow Slip ◽  
Slow Slip Events ◽  
Actual Distribution ◽  
Frictional Properties ◽  
Data Analyses ◽  
Bungo Channel ◽  
Megathrust Faults ◽  
Gnss Data

Summary A variety of slow slip events at subduction zones have been observed. They can be stress meters for monitoring the stress state of megathrust faults during their earthquake cycles. In this study, we focus on long-term slow slip events (LSSEs) recurring at downdip portions of megathrust faults among such slow earthquakes. Data analyses and simulation studies of LSSEs have so far been executed independently. In atmosphere and ocean sciences, data assimilations that optimally combine data analyses and simulation studies have been developed. We develop a method for estimating frictional properties and monitoring slip evolution on an LSSE fault, with a sequential data assimilation method, the ensemble Kalman filter (EnKF). We executed numerical twin experiments for the Bungo Channel LSSE fault in southwest Japan to validate the method. First, based on a rate- and state-dependent friction law, we set a rate-weakening circular LSSE patch on the rate-strengthening flat plate interface, whose critical nucleation size is larger than that of the patch, and reproduced the observed Bungo Channel LSSEs with recurrence times of approximately 7 yr and slip durations of 1 yr. Then, we synthesized the observed data of surface displacement rates at uniformly distributed stations with noises from the simulated slip model. Using our EnKF method, we successfully estimated the frictional parameters and the slip rate evolution after a few cycles. Secondly, we considered the effect of the megathrust fault existing in the updip portion of the LSSE fault, as revealed by kinematic inversion studies of Global Navigation Satellite System (GNSS) data and added this locked region with a slip deficit rate in the model. We estimated the slip rate on the locked region only kinematically, but the quasi-dynamic equation of motion in each LSSE fault cell includes the stress term arising from the locked region. Based on this model, we synthesized the observed surface displacement rate data for the actual distribution of GNSS stations and executed EnKF estimations including the slip rate on the locked region. The slip rate on the locked region could be quickly retrieved. Even for the actual distribution of GNSS stations, we could successfully estimate frictional parameters and slip evolution on the LSSE fault. Thus, our twin numerical experiments showed the validity of our EnKF method, although we need further studies for actual GNSS data analyses.

scholarly journals Detection and Characterization of Meteotsunamis in the Gulf of Genoa

2019 ◽  
Vol 7 (8) ◽  
pp. 275 ◽  
Author(s):  
Picco ◽  
Schiano ◽  
Incardone ◽  
Repetti ◽  
Demarte ◽  
...  
Keyword(s):  
Sea Level ◽  
Atmospheric Pressure ◽  
Tidal Elevation ◽  
Port Management ◽  
Time Frequency ◽  
Abrupt Jump ◽  
Level Data ◽  
Work Planning

A long-term time series of high-frequency sampled sea-level data collected in the port of Genoa were analyzed to detect the occurrence of meteotsunami events and to characterize them. Time-frequency analysis showed well-developed energy peaks on a 26–30 minute band, which are an almost permanent feature in the analyzed signal. The amplitude of these waves is generally few centimeters but, in some cases, they can reach values comparable or even greater than the local tidal elevation. In the perspective of sea-level rise, their assessment can be relevant for sound coastal work planning and port management. Events having the highest energy were selected for detailed analysis and the main features were identified and characterized by means of wavelet transform. The most important one occurred on 14 October 2016, when the oscillations, generated by an abrupt jump in the atmospheric pressure, achieved a maximum wave height of 50 cm and lasted for about three hours.

scholarly journals Changing storminess? An analysis of long-term sea level data sets

1999 ◽  
Vol 11 ◽  
pp. 161-172 ◽  
Author(s):  
W Bijl ◽  
R Flather ◽  
JG de Ronde ◽  
T Schmith
Keyword(s):  
Sea Level ◽  
Data Sets ◽  
Level Data
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