Helmert variance component estimation with non-negative constraint of covariance matrix

doi:10.11947/j.AGCS.2022.20200333

Abstract

Abstract: How to determine the relative weight ratio in geodetic data inversion among various datasets is one of the challenging topics. In recent years, Helmert variance component estimation method (HVCE) has been widely used in the field of relative weights of joint inversion slip distributions due to its advantage of simultaneously estimating the scale factors between virtual and actual observations. However, the HVCE method sometimes suffers from the occurrence of negative variance. To overcome these problems, we propose linear inequality constraints based helmert variance component estimation method (LC-HVCE). We carry out synthetic experiment to verify the effectiveness and robustness of the LC-HVCE method and estimate the uncertainty of LC-HVCE method by Monte carlo method. We apply the proposed method to invert for the source parameters of the 2009 Mw6.3 L'Aquila earthquake (Italy). We find that LC-HVCE method is more applicable and stability than HVCE method in determining relative weight ratio and regularization parameter.

Key words: Helmert variance component estimation, relative weight ratio, regularization parameter, linear inequality constraints

CLC Number:

P228

WANG Leyang, ZHAO Xiong, XU Wenbin, WANG Chisheng, FANG Nan, XIE Lei. Helmert variance component estimation with non-negative constraint of covariance matrix[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(3): 351-360.

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