Error Analysis Of 2-d Ground Deformation Field Based On The Minimum Acceleration Combination Technique And Research On Comprehensive Risk Assessment Method Of Land Subsidence

Deformation of major urban infrastructures and urban waterlogging are common challenges affecting the typical coastal cities in BRICS countries.Land subsidence caused by land reclamation,subway and high-rise building construction,and urban flooding caused by storm surges are seriously threatening the public safety of coastal cities.Thus,it is necessary to monitor the deformation of cities and flood control projects and conduct a risk assessment of ground subsidence.In this paper,the typical coastal cities of BRICS countries,St.Petersburg,Russia,and Shanghai,China,are used as research areas,and this paper mainly conducts the following research works:(1)A Modified Minimum Acceleration Combination Technique(MMinA)is developed to obtain the two-dimensional time-series deformation field of St.Petersburg city and St.Petersburg Flood Prevention Facility Complex(FPFC).The results show that the subsidence of St.Petersburg is mainly distributed in land-reclaimation area and along the Metro Lines,with the mean subsidence velocities of 20 mm/year and 12 mm/year,respectively.The MMinA decomposition results shows that the deformation of the city is mostly vertical with respect to the east-west direction.Of great concern is the study of the stability of the FPFC that was built to prevent the city from extreme flood events.In particular,our analysis has revealed that the D3 section of the facility is more prone to deformation and that the lateral movements also affect the structure,which is of high relevance for urban planners and security practitioners.(2)The error distribution law of MMinA was found with the using of an error analysis method proposed in this work.Subsequently an error analysis of the MMinA decomposition results reveals that the accuracy of the Up-Down deformation components is better than that of the East-West deformation components and it improves as the average spatial coherence of the selected pixels increases.(3)A comprehensive risk assessment model of land subsidence was established with the using of multi-source social perception data,high-resolution Small BAseline Subset(SBAS)deformation time series and fuzzy neural network technology.The model results show that the potential medium and high risks of land subsidence in Shanghai are mostly located in land-reclaimation areas.In the central urban area,Huangpu District has a higher proportion of areas with medium and high-level land subsidence risks than other administrative districts.As for the suburbs,Minhang District has the smallest proportion of areas with medium and high-level land subsidence.And the model also shows that area ratio of each land subsidence risk level in Shanghai is approximately: stable(?): low risk(II): medium risk(III): high risk(IV)= 65: 26: 6: 3.