Assessment of ground-motion prediction equations using observations and information theory: application to the Northeastern Tibetan Plateau

Ground-motion prediction equations (GMPEs) are fundamental to seismic hazard assessment, yet
their applicability is difficult to evaluate in tectonically complex regions where instrumental
observations are sparse or absent, such as the northeastern Tibetan Plateau. We evaluate
representative GMPEs using two approaches: residual-based error metrics and an information-
theory-based evaluation. The two recent earthquakes, the 2022 Menyuan and 2023 Jishishan events,
provide an opportunity to examine model behavior under observed conditions. The information-
theory-based approach is first applied to these two earthquakes to verify feasibility and to compare
with residual-based outcomes, and is then extended to two large historical earthquakes in the region,
the 1920 Haiyuan earthquake and the 1927 Gulang earthquake, to assess stability under extrapolative,
data-limited conditions. Across these applications, the information-theory-based evaluation yields
consistent rankings and stable weights, whereas the residual analysis captures event-specific
variability. Together, the two approaches provide complementary insight into model applicability and
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offer a practical basis for ground-motion assessment and extrapolation in tectonically complex and
observationally sparse regions.