Kriging ground settlement time domain monitoring method based on simulated annealing method

Abstract

The invention discloses a Kriging ground settlement time domain monitoring method based on a simulated annealing method. The method comprises the following steps of: extracting monitoring information, performing normal conversion, and determining the dimension scale of equivalent dimension dynamic prediction; dividing time groups, calculating a variable function value which corresponds to each time group, selecting a spherical model as a variable function model, and fitting the variable function model by using the simulated annealing method; and drawing a variable function curve, calculating Kriging weight coefficients, calculating an estimation value of Kriging interpolation at a predicted moment, performing iterative calculation, interpolating and encrypting grids in a time domain, and generating a settlement monitoring result at a quasi monitoring moment. The Kriging ground settlement time domain monitoring method has the advantages that high-accuracy prediction can be performed according to the characteristic of ground settlement data abnormal distribution, the abnormal situation of monitoring points can be effectively and truly reflected, and the phenomenon of falling of a solution into a local extremum caused by high dependence of a linear inversion method result on selection of an initial model can be avoided.

Description

technical field [0001] The invention relates to a monitoring model in the field of geographic information, in particular to a Kringing land subsidence time domain monitoring method based on a simulated annealing method. Background technique [0002] Land subsidence is a common and increasingly obvious geological phenomenon. It is a kind of environmental geological change caused by regional ground elevation drop, which mostly occurs in densely populated and industrially developed cities. Urban land subsidence monitoring is an important means to protect people's lives and properties and reduce economic losses. The analysis and processing of monitoring data is the scientific basis for judging the quality of an urban environment. Therefore, while the city is developing, it is necessary to monitor the subsidence of its bottom surface, grasp the first-hand information in time, and conduct scientific analysis on the observation data to discover possible hidden dangers in time, so ...

AI Technical Summary

Problems solved by technology

[0003] On the other hand, in the Kriging model, the non-normality of the monitoring data will lead to the gradual accumulation of disturbance information, so that the variation function cannot truly reflect the distribution characteristics of the settlement data. The traditional variation function fitting model is based on the least squares criterion , which will significantly amplify the measurement error when processing data

Images