Prevailing Bayesian parameter inversion techniques have often been marred by extended computational durations,diminished computational precision and sub-optimal accuracy.Hence,a hy-brid surrogate model underpinned by the multiple attempts of differential evolution adaptive Metropolis(MT-DREAM(ZS))algorithm was introduced,which offered a more scientifically grounded approach for determining the weight coefficients of individual models,and was modified through Pareto optimiza-tion-based dynamic weight coefficient multi-objective optimization.Three distinct machine learning methodologies including multivariate adaptive regression splines,artificial neural network random forest,and random forest were integrated into the Bayesian framework to establish a composite model.Additionally,the posterior distribution of seepage parameters was deduced,while thoroughly accounting for uncertainties present in the inversion procedure.Combined with the monitoring data of the actual project,the gap between this combinatorial surrogate model and other models was compared and analyzed by calculating the prediction performance index R2 and RMSE.Research findings substan-tiate that the hybrid surrogate model,coupled with the novel technique for weight determination of indi-vidual models,boasts superior fitting precision and predictive efficacy.Compared with the traditional method,the improvement rate is 15.00％-20.00％on average.By applying the inverted seepage pa-rameters to simulation experiments,a new approach is provided for the development of dam seepage detection research.
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