PARAMETRIC STUDY ON GROUND SURFACE DEFORMATION FORMING PULL-APART STRUCTURE CAUSED BY FAULT MOVEMENT

2021 ◽  
Vol 77 (4) ◽  
pp. I_14-I_23
Author(s):  
Hidetaka SAOMOTO
Keyword(s):  
Parametric Study ◽  
Surface Deformation ◽  
Ground Surface ◽  
Fault Movement

scholarly journals Imaging ground surface deformations in post-disaster settings via small UAVs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Richard L. Ybañez ◽  
Audrei Anne B. Ybañez ◽  
Alfredo Mahar Francisco A. Lagmay ◽  
Mario A. Aurelio
Keyword(s):  
Surface Deformation ◽  
Ground Surface ◽  
Point Clouds ◽  
Lateral Spreading ◽  
Fault Movement ◽  
Field Surveys ◽  
3D Point Clouds ◽  
Vertical Displacements ◽  
Resolution Imaging ◽  
Post Disaster

AbstractSmall unmanned aerial vehicles have been seeing increased deployment in field surveys in recent years. Their portability, maneuverability, and high-resolution imaging are useful in mapping surface features that satellite- and plane-mounted imaging systems could not access. In this study, we develop and apply a workplan for implementing UAV surveys in post-disaster settings to optimize the flights for the needs of the scientific team and first responders. Three disasters caused by geophysical hazards and their associated surface deformation impacts were studied implementing this workplan and was optimized based on the target features and environmental conditions. An earthquake that caused lateral spreading and damaged houses and roads near riverine areas were observed in drone images to have lengths of up to 40 m and vertical displacements of 60 cm. Drone surveys captured 2D aerial raster images and 3D point clouds leading to the preservation of these features in soft-sedimentary ground which were found to be tilled over after only 3 months. The point cloud provided a stored 3D environment where further analysis of the mechanisms leading to these fissures is possible. In another earthquake-devastated locale, areas hypothesized to contain the suspected source fault zone necessitated low-altitude UAV imaging below the treeline capturing Riedel shears with centimetric accuracy that supported the existence of extensional surface deformation due to fault movement. In the aftermath of a phreatomagmatic eruption and the formation of sub-metric fissures in nearby towns, high-altitude flights allowed for the identification of the location and dominant NE–SW trend of these fissures suggesting horst-and-graben structures. The workplan implemented and refined during these deployments will prove useful in surveying other post-disaster settings around the world, optimizing data collection while minimizing risk to the drone and the drone operators.

3D Visual Technology of Geo-Deformation Disasters Induced by Mining Subsidence Based on ArcGIS Engine

2012 ◽  
Vol 500 ◽  
pp. 428-436 ◽  
Author(s):  
Ke Ming Yang ◽  
Jun Ting Ma ◽  
Bo Pang ◽  
Yi Bin Wang ◽  
Ran Wang ◽  
...  
Keyword(s):  
Coal Mining ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Ground Surface ◽  
Horizontal Displacement ◽  
Mining Subsidence ◽  
Underground Coal Mining ◽  
Vertical Movements ◽  
Probability Integral Method ◽  
Arcgis Engine

Mining subsidence often produces significant horizontal and vertical movements at the ground surface, the surface deformation induced by underground coal mining can be predicted by probability integral method, and the surface geo-deformation disasters can be visualized based on GIS components. A three dimensional (3D) visualizing system of surface geo-deformation information is designed and developed with ArcGIS Engine and C# in the study. According to the surface deformation-predicted data induced by underground coal mining in Guobei Coalmine of Huaibei mine field, the extents and degrees of ground deformation disasters are visualized in 3D views for surface vertical subsidence, slope, curvature, horizontal displacement and horizontal strain based on the GIS-developed application platform.

EnKF estimation of the viscoelastic deformation and the viscosity

2020 ◽  
Author(s):  
Makiko Ohtani
Keyword(s):  
Data Assimilation ◽  
Crustal Deformation ◽  
Surface Deformation ◽  
Synthetic Data ◽  
Ground Surface ◽  
Inelastic Strain ◽  
Large Earthquake ◽  
Forward Model ◽  
Viscoelastic Deformation ◽  
Large Earthquakes

<p>Following large earthquakes, postseismic crustal deformations are often observed for more than years. They include the afterslip and the viscoelastic deformation of the crust and the upper mantle, activated by the coseismic stress change. The viscoelastic deformation gives the stress change on the neighboring faults, hence affects the seismic activity of the surrounding area, for a long period after the large earthquake. So, estimating the viscoelastic deformation after the large earthquakes is important.</p><p>In order to estimate the time evolution of the viscoelastic deformation after a large earthquake, we also need to know the viscoelastic structure around the area. Recently, the Ensemble Kalman filter method (EnKF), a sequential data assimilation method, starts to be used for the crustal deformation data to estimate the physical variables (van Dinther et al., 2019, Hirahara and Nishikiori, 2019). With data assimilation, we get a more provable estimation by combining the data and the time-forward model than only using the data. Hirahara and Nishikiori (2019) used synthetic data and showed that EnKF could effectively estimate the frictional parameters on the SSE (slow slip event) fault, addition to the slip velocity. In the present study, I applied EnKF to estimate the viscosity and the inelastic strain after a large earthquake, both the physical property and the variables.</p><p>First, I constructed the forward model simulating the evolution of the viscoelastic deformation, following the equivalent body force method (Barbot and Fialko, 2010; Barbot et al., 2017). This method is appropriate for applying EnKF, because the ground surface deformation rate is represented by the inelastic strain at the moment, and the history of the strain is not required. Then, we applied EnKF based on the forward model and executed some numerical experiments using a synthetic postseismic crustal deformation data.</p><p>In this presentation, I show the result of a simple setting. I assumed the medium to be two layers with a homogeneous viscoelastic region underlying an elastic region. The synthetic data is made by giving a slip on a fault at time <em>t</em> = 0 and simulating the time evolution of the ground surface deformation. For assimilation, I assumed that the slip on the fault and the stress distribution just after the large earthquake is known. Then we executed the assimilation every 30 days after the large earthquake. I found that I can get a good estimation of the viscosity after <em>t</em> > 150 days.</p>

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