Thermal and Mechanical Controls on Pacific Plate Flexure under Seamount Loading

Understanding the elastic behavior of the oceanic lithosphere is crucial for interpreting plate dynamics and rheology. While various methods exist to estimate the lithosphere's ability to deform under load, the factors controlling this deformation across different tectonic settings remain poorly quantified.

We present a three-stage analysis to systematically evaluate controls on lithospheric flexure across the Western Pacific. First, we calculate a suite of metrics that characterize the elastic deformation properties of the lithosphere using gravity and bathymetry data. Second, we develop a random forest regression framework, a type of machine learning model, to reconstruct these observed deformation properties using a range of geophysical parameters, including gravity, bathymetry, sediment thickness, oceanic crustal age, heat flow, and hotspot proximity. By analyzing the feature importance within this model, we quantify the relative influence of each parameter on lithospheric deformation. Finally, we apply this framework to different tectonic settings (mid-ocean ridges, oceanic plateaus, abyssal plains, and seamount chains) to examine how the controlling factors vary by geological context.

This quantitative assessment, leveraging machine learning, advances our understanding of oceanic plate rheology and provides a framework for interpreting lithospheric behavior across different tectonic environments. The results have important implications for understanding plate dynamics and the evolution of the Pacific lithosphere.