Optimizing Geophysical Inversion: Versatile Regularization and Prior Integration Strategies for Electrical and Seismic Tomographic Data

Abstract

The increasing demand for high-resolution subsurface imaging has driven significant advances in geophysical inversion methodologies. Despite the availability of various software packages for electrical resistivity tomography (ERT), time-domain induced polarization (TDIP), and seismic refraction tomography (SRT), significant challenges remain in selecting optimal regularization parameters and in the effective incorporation of prior information into the inversion process. In this study, we propose new strategies to address these critical issues by developing versatile and flexible tools for electrical and seismic tomographic data inversion. Specifically, we introduce two automated procedures for regularization parameter selection: a full loop method (fixed-λ optimization) where the regularization parameter is kept constant during the inversion process, and a single-inversion approach (automaticLam) where it varies throughout the iterations. Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. Applications to synthetic, laboratory, and real-world case studies validate the efficacy of our strategies, showcasing their potential to enhance the robustness of geophysical models and standardize the inversion process, ensuring its independence from operator decisions.