Abstract: Traditional initial geostress inversion methods suffer from limited boundary condition selection capability, susceptibility to overfitting, and challenges in resolving interactions among multiple boundaries. To overcome these drawbacks, a two-stage inversion method based on the least absolute shrinkage and selection operator (LASSO) and ordinary least squares (OLS) is proposed. First, the candidate boundary condition stress matrix and the measured stress matrix are normalized using the Frobenius norm to eliminate the effect of magnitude differences among boundary conditions. LASSO regression, with its L1 regularization constraint, is then applied to identify key influencing factors from the regression coefficient path diagram, thereby eliminating redundant and weakly correlated terms. For the selected core variables, OLS regression is subsequently employed to achieve unbiased estimation, constructing a geostress inversion model that balances sparsity and accuracy. The results show that: (1) LASSO regression selects five key factors from 11 candidate boundary conditions, significantly reducing model complexity. (2) When the regularization parameter is chosen within the standard error range, the fitting results maintain a high multiple correlation coefficient (R=0.995 2), demonstrating that the selected boundary conditions effectively capture the characteristics of the initial geostress field. (3) The inversion model screened via LASSO regression exhibits improved stability and physical reasonableness when analyzing interactions among multiple boundaries. (4) Compared with traditional methods, this approach effectively avoids overfitting in initial geostress inversion and enhances the robustness of the results.

Key words: pumped storage power station, initial geostress field, least absolute shrinkage and selection operator, least angle regression, ordinary least squares; cross-validation