Interpreting deformation results of geodetic network points using the strain models based on different estimation methods

Abstract

Geodetic Networks designed as Deformation Networks or Continuous Networks are observed in different epochs/ sessions and evaluated as a function of time. Those can be design as global GNSS networks for aim monitoring active tectonic movements or as regional densification geodetic and deformation networks for monitoring local earthquakes and surface movements. The areas covered geodetic networks are assumed as any surface on ellipsoid or sphere. Characteristics of surfaces are analyzed with Geometric Strain Models using deformation data on surface points. In this case, effect rates on geodetic network area are determined from local surface movements or regional active earthquakes and interpreted as experimental. On the other hand, undetermined outliers by model hypothesis test affect coordinateunknowns separately. Outliers cause deformations in certain magnitude on networks points. Therefore, network points strain in different rates and directions. Query of maximum affects caused by these strain rates is a referenced reliability method called "Robustness Analysis in Geodetic Networks". Mentioned strain rates are modelled by various estimation methods. Thus, deformation results could be interpreted together by the obtained strain components and deformation vector. In this paper, possible strain components belonging to network points are determined with methods of L1 Norm, Least Median Squares (LMS) and Least Squares Estimation (LSE). These estimation methods are tested on KOUSAGA (Kocaeli University Permanent GPS Network). Strain components are estimated by use polyhedrons covered by network points. Obtained results are compared and analyzed according to weakness and strengths of proposed estimation methods.

GNSS and Sentinel-1 InSAR Integrated Long-Term Subsidence Monitoring in Quetta and Mastung Districts, Balochistan, Pakistan

“…The Global Navigation Satellite System (GNSS) is one of the most precise and reliable instruments employed for decades to monitor land displacement in 3D space and time. GNSS has a very high temporal resolution; however, its spatial resolution is not adequate enough due to point-based measurements [9][10][11]. In contrast, the Interferometric Synthetic Aperture Radar (InSAR) technique provides high-precision displacement information along the line of sight (LOS), covering thousands of square kilometres in all weather conditions.…”

Study of the seismic activity of the Almalyk-Angren industrial zone based on lineament analysis

“…The understanding and prediction of earthquakes require diverse monitoring methods and data analysis. Specialists are developing and researching lineament systems in addition to alternative methods like satellite interferometry, GPS observations, heat flow tracking, and monitoring the environment, such as the ionosphere and ice cover of water bodies [1][2][3]. Lineament analysis identifies geological faults and cracks that may indicate seismic activity and potential earthquake sources, offering a means of earthquake prediction.…”

“…International Journal of Engineering and Geosciences, 2024, 9(1),[1][2][3][4][5][6][7][8][9][10][11] …”

Deriving 3-D Surface Deformation Time Series with Strain Model and Kalman Filter from GNSS and InSAR Data

“…The strain model is based on elasticity theory and can be used to model stress-strain relationships between points in a geographic space [25]. The strain model introduced in KFInSAR can reduce the high-frequency noises of InSAR, such as atmospheric errors.…”