Moho topographic inversion of the South China Sea based on genetic algorithm

Abstract

The Parker-Oldenburg method is a commonly used classical interface inversion method for Moho topographic inversion. However, this method is excessively reliant on two hyperparameters − the Moho density contrast and the average Moho depth. Due to the failure to take into account the effect of non-linear terms and computational inefficiencies, this previous method leads to a significant bias in the hyperparameters estimation, which renders it impossible to invert the finer Moho topography. To address this issue, we propose a new method that utilizes the genetic algorithm to estimate more accurate hyperparameters. Synthetic test results illustrate that the differences of the estimated Moho density contrast and average Moho depth from our method and the true values are only 0.044 g/cm³ and 0.729 km, respectively. Compared with the improved Bott’s method, the errors were reduced by 12.28 % and 2.23 %, respectively. To further illustrate the effectiveness of our method, we apply this method to the Southwestern Sub-basin of the South China Sea, where the Moho density contrast and average Moho depth are determined to be 0.61 g/cm³ and 19.18 km, respectively by imposing seismic data constraints. The Moho topography is then inverted based on these determinations, revealing that the Moho topography ranges from 6.3 km to 24.9 km in the study area and exhibits pronounced undulations. Compared to other Moho topography models, our Moho topography is more accurate.