Leveraging the Physics of Jerk: An Automated Framework for Recovering Permanent Displacement from Seismic Records

Abstract

Coseismic permanent ground displacement is essential for understanding earthquake rupture mechanisms and assessing seismic hazards. However, traditional geodetic methods are often limited by sparse spatiotemporal coverage and high costs. Strong-motion records offer a valuable supplement but are frequently contaminated by baseline offsets. Existing baseline correction methods largely rely on visual inspection and empirical image-based judgments, lacking a clear physical foundation and sufficient robustness. As a result, they are ill-suited for the automated processing of large-scale strong-motion datasets, and the valuable displacement information contained in these records remains underutilized. This study breaks away from conventional approaches by reformulating baseline correction as an inverse problem in robot trajectory planning and proposing a fully automated correction framework based on jerk constraints. The method achieves fully automated processing and effectively handles challenging scenarios such as velocity pulses, complex waveform distortions, and nonideal instrument states. Its strong cross-event adaptability is validated using records from the Chi-Chi, Wenchuan, and Kahramanmaraş earthquakes. By providing a physical interpretation of baseline offsets, this work eliminates the dependence on empirical, image-based adjustments and offers a novel, physically grounded approach for strong-motion signal processing.