Research Of Particle Swarm Algorithm In Geodetic Inversion

Geodetic inversion is a marginal subject that studies the evolutionary characteristics and laws of objective deformation on the earth's surface by using observational data obtained from geodetic surveys,deriving the physical parameters and characteristics of the earth's interior.At present,geodesy inversion is becoming the most basic and important method for geodesy to go deep into the field of geosciences,explore the mysteries of the earth,and study its mechanism through complex geodynamic phenomena to explain regional or global earth events.Geodetic inversion includes many aspects,and seismic fault parameter inversion and volcanic pressure source parameter inversion are important parts of geodetic inversion and are the focus of this paper.The methods commonly used in current geodetic inversion include least squares algorithm,global least squares algorithm,genetic algorithm,simulated annealing algorithm,particle swarm algorithm,and so on.Considering that the particle swarm method has the advantages of high accuracy and few parameters when used in inversion,this paper studies the volcanic pressure source parameters and seismic fault parameters using the particle swarm algorithm in geodetic inversion.The main contents studied in this article are as follows:1.Aiming at when the Least squares method,Total Least squares method are used to invert the pressure source parameters of the volcano Mogi model,the deviation is easy to occur in the linearization process,which leads to the problem that the obtained parameter solution deviates from the true value.This paper analyzes the non-linear characteristics of the Mogi model.At the same time,it considers that the particle swarm algorithm has the advantages of fast speed and high accuracy in solving non-linear problems.Combining the two results in a particle swarm optimization algorithm for Mogi model parameters inversion.The simulation results and the verification of real volcanic inversion show that the results obtained by the proposed method are improved by 1 to 2 orders of magnitude and closer to the true value in the simulation example compared with other methods.In real volcanic inversion.The fitting results are closer to the surface observations.It shows that the proposed method has applicability and effectiveness in the volcanic Mogi model inversion.2.Aiming at the problem of low accuracy of particle swarm algorithm currently used in fault parameter inversion.This paper analyzes the nonlinear characteristics of the earthquake fault parameter inversion and analyzes the characteristics of the basic particle swarm optimization algorithm.The problem is that the basic particle swarm optimization algorithm is easy to fall into the local optimal solution in dealing with highly nonlinear problems.Considering the interaction between the local optimal solution and the global optimal solution in the searching process of the traditional particle swarm optimization,the inertia factor that affects the particle velocity is adjusted by segmentation,and the acceleration factors that affect the global optimal solution and the local optimal solution are also adjusted.A particle swarm optimization algorithm for dynamically modifying parameters dynamically to find a suitable value for fault source parameters inversion is proposed.In this paper,the algorithm is applied to the inversion of fault parameters of simulated and L'Aquila real earthquakes.The results show that the proposed algorithm can obtain global optimal fault parameters and is stable in simulation experiments.In the simulation example,the values of fault dip and rake obtained by the algorithm are closer to the true value than the MPSO method.In the case of the 2009 L'Aquila earthquake,the RMS(predictions of our model obtained by the algorithm)of this paper is 0.57 mm,batter than the 6.7mm of MPSO,which demonstrates the practical value of the algorithm in this paper.3.A particle swarm optimization(pso)algorithm combined with the black hole strategy is proposed to solve the problem that the genetic algorithm and simulated annealing method used in the inversion of conventional geophysical parameters have many control parameters,low precision and slow inversion speed.Through simulation experiments,it is found that the black hole particle swarm optimization(bpso)algorithm has the advantages of high precision and high computational efficiency in the inversion of geophysical model parameters,which is applicable to the estimation of volcanic magma sac parameters,compared with the conventional nonlinear inversion algorithm.In this paper,black hole particle swarm optimization(pso)combined with elastic composite dislocation model(CDM),Yang model and Mogi model were used to retrieve the magmatic capsule parameters of 2015 Calbuco volcano eruption in Chile.The results show that the single-source elastic composite dislocation model can better fit the observed ground deformation data than the Mogi model.The parameters of the model show that the pressure source of Calbuco volcano's magma capsule is located within 1.5 km in the east direction of the crater and 1 km in the north direction.The depth of the center is about 9 km underground.The RMSE of this model is 1.14 cm,which is better than the inversion results of Mogi model.The magma eruption volume obtained by using the inversion of elastic consistent dislocation model is smaller than the results of Mogi model,but it is similar to the previous research results.Therefore,it is considered that the shape of the pressure source is more accurate for the inversions of the elastic consistent dislocation model,and the magma eruption quantity of the Mogi model is better than that of the elastic consistent dislocation model.