Inversion and precision estimation of earthquake fault parameters based on scaled unscented transformation and hybrid PSO/Simplex algorithm with GPS measurement data

• Published in: Measurement : journal of the International Measurement Confederation Vol. 153; p. 107422
• Main Authors: Wang, Leyang, Ding, Rui
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.03.2020; Elsevier Science Ltd
• Subjects: Algorithms; Computer simulation; Curve fitting; Earthquakes; Fault diagnosis; Global positioning systems; GPS; GPS data; Hybrid PSO/simplex algorithm; Inversion of fault parameters; Monte Carlo simulation; Optimization; Order parameters; Parameter estimation; Particle swarm optimization; Precision estimation; Satellite navigation systems; Scaled unscented transformation; Precision estimation; GPS data; Hybrid PSO/simplex algorithm; Inversion of fault parameters; Scaled unscented transformation
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2019.107422

•Prove that SUT method can be applied to nonlinear optimal algorithm.•First use SUT for fault parameters inversion and its precision estimation.•Adjust the observations to obtain better fault parameter estimate.•Analyze the effect of measurement data on the fault parameters estimate. The Scaled Unscented Transformation (SUT) method based on deterministic sampling strategy is introduced to nonlinear inversion and precision estimation of earthquake fault parameters. Firstly, the SUT method is used to estimate the precision of the curve fitting parameters obtained by Particle Swarm Optimization (PSO) algorithm and compared with least square method and Monte Carlo method to verify the effectiveness of the proposed method. Considering the inevitable error in the observations, we preprocess the simulated GPS data to obtain the approximate mean of observations, then contrast and analyze the fault parameter estimatesand precision information obtained by SUT method and Monte Carlo method based on the hybrid PSO/Simplex algorithm (MPSO). The influence of the “adjusted” observations, positions and number of the observation points and the level of noise added to deformation observations are also considered. Finally, the methods in this paper are applied to the Lushan (China) earthquake. The experimental results show that both SUT method and Monte Carlo method can obtain better parameter estimates with “adjusted” observations; SUT method can obtain more accurate precision information of fault parameters; Monte Carlo method can only judge mean square errors of parameters from the order of magnitude and the correlations between parameters are not accurate; When using GPS measurement data to invert the fault parameters, the positions and number of GPS points have certain influence on some fault parameters; When the noise added to the GPS measurement data increases, the fitting of the fault parameter estimate become worse and the precision of fault parameter estimate decreases.