Comprehensive Evaluation and Comparison of Nonlinear Inversion Algorithms

  Objectives  As nonlinear models become more complex and data sources become more abundant, people have higher and higher requirements for nonlinear methods and precision.However, the existing research mainly considered the precision evaluation of the algorithm, and ignored how to comprehensively evaluate the applicability of the algorithm based on precision information obtained by calculation.

  Methods  Considering the above problems, we propose a method to evaluate the advantages of algorithms based on precision information—non-linear comprehensive evaluation method. The method firstly evaluates the accuracy by the traditional Monte Carlo (MC) method, and uses the calculated deviation and the median error as the evaluation index, and calculates the comprehensive evaluation value according to the proposed comprehensive evaluation formula. We use the proposed method to evaluate the precision information of simulated annealing algorithm (SA), genetic algorithm (GA) and neural network algorithm (NNA) in volcano compound dislocation model (CDM) and seismic Okada model inversion.

  Results  The experimental results show that whether it is based on the CDM model or the Okada model, the parameter estimation gap between the above three methods is small, but the precision of the different methods varies greatly. The MC is used to calculate the precision of SA, GA and NNA. It is found that in the volcano CDM, mean square errors in some parameters calculated by SA and GA are relatively large, and the comprehensive evaluation values 11.398 8 and 3.982 0 of SA and GA are larger than the comprehensive evaluation value 3.613 1 of NNA. In the Okada model, the SA has a higher mean square error than GA and NNA, and in the Lushan earthquake, the comprehensive evaluation value of the SA is 11.656 2, which is much larger than the comprehensive evaluation values 4.060 4 and 3.625 4 of the GA and NNA.

  Conclusions  The precision information of the NNA is higher when the proposed method is used for evaluation, and the result is more convincing, followed by GA, and SA has lower precision.