Time prediction of an onset of failure in a sandy model slope based on the monitoring of the groundwater level and the surface displacement at different locations

Author(s):  
K Sasahara ◽  
T Ishizawa
2018 ◽  
Vol 13 (1) ◽  
pp. 13-25
Author(s):  
Katsuo SASAHARA ◽  
Naoki IWATA ◽  
Naotaka KIKKAWA ◽  
Nobutaka HIRAOKA ◽  
Kazuya ITOH

2021 ◽  
Author(s):  
Yutaro Shigemitsu ◽  
Kazuya Ishitsuka ◽  
Weiren Lin

<p>The 2018 northern Osaka earthquake with a magnitude 6.1 earthquake struck on June 18, 2018 in northern Osaka, causing enormous damage. SAR interferometry using satellite synthetic aperture radar (SAR) data can detect surface displacement distribution over a wide area and is effective for observing surface displacement during an earthquake. On the other hand, it is also important to observe the tendency of long-term surface displacement around active faults on a yearly basis in order to monitor the deformation at and around active faults. In this study, we used persistent scatter SAR interferometry (PS-InSAR) to clarify the recent surface displacement including before and after the 2018 northern Osaka earthquake near the Arima-Takatsuki Fault Zone and the Mt. Rokko active segment, near the epicenter of the earthquake. PS-InSAR analysis is a method that analyzes coherent pixels only, and can extract surface displacements with less noise than the conventional two-pass SAR interferometry. By using Sentinel-1 data, we expect to understand a long-term surface displacement and temporal changes in displacement pattern by comparing with the results using other satellites in previous studies. As a result of our analysis, we found that (i) ground subsidence occurred near the Mt. Rokko active segment, (ii) subsidence or eastward displacement occurred in the eastern part of the Takarazuka GNSS station, (iii) surface displacement in the wedge-shaped area located between the Arima-Takatsuki Fault Zone and the Mt. Rokko active segment is suggested to be caused by groundwater level changes, (iv) groundwater level changes may have caused surface displacement considered to be uplift in the wide area between the Ikoma Fault Zone and Uemachi Fault Zone, and (v) slip of the source fault may have caused surface displacement around the epicenter of the 2018 northern Osaka earthquake. Furthermore, we validated the estimated surface displacements by comparison with GNSS measurements and previous studies. These results suggest that surface displacement near the Arima-Takatsuki fault zone was caused by the 2018 northern Osaka earthquake. In order to reveal the mechanism of surface displacement in the vicinity of the fault, it is necessary to continue to monitor the surface displacement in this area using time-series SAR interferometry.</p><p> </p><p> </p><p>We acknowledge Sentinel-1 data provided from the European Space Agency (ESA) based on the open data policy.</p>


2021 ◽  
Vol 13 (21) ◽  
pp. 4391
Author(s):  
Mohamed Mourad ◽  
Takeshi Tsuji ◽  
Tatsunori Ikeda ◽  
Kazuya Ishitsuka ◽  
Shigeki Senna ◽  
...  

We present a novel approach to mapping the storage coefficient (Sk) from InSAR-derived surface deformation and S-wave velocity (Vs). We first constructed a 3D Vs model in the Kumamoto area, southwest Japan, by applying 3D empirical Bayesian kriging to the 1D Vs profiles estimated by the surface-wave analysis at 676 measured points. We also used the time series of InSAR deformation and groundwater-level data at 13 well sites covering April 2016 and December 2018 and estimated the Sk of the confined aquifer. The Sk estimated from InSAR, and well data ranged from ~0.03 to 2 × 10−3, with an average of 7.23 × 10−3, values typical for semi-confined and confined conditions. We found a clear relationship between the Sk and Vs at well locations, indicating that the compressibility of an aquifer is related to the stiffness or Vs. By applying the relationship to the 3D Vs model, we succeeded in mapping the Sk in an extensive area. Furthermore, the estimated Sk distribution correlates well with the hydrogeological setting: semi-confined conditions are predicted in the Kumamoto alluvial plain with a high Sk. Our approach is thus effective for estimating aquifer storage properties from Vs, even where limited groundwater-level data are available. Furthermore, we can estimate groundwater-level variation from the geodetic data.


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