Physics‐Informed Machine Learning Framework to Retroactively Estimate Mantle Thermal Convection From Partial Geophysical Observations

Abstract

Mantle convection drives the solid Earth, powering plate motions, volcanism, and earthquakes while regulating planetary heat loss. Reconstructing its history is hampered by sparse, noisy observations concentrated near the surface and the present day. Here I develop an inverse physics‐informed neural network framework to estimate mantle thermal convection as continuous space‐time fields. Twin experiments assimilate irregular near‐surface kinematics from tracer trajectories together with a terminal interior temperature structure, used as a proxy for present‐day imaging. The method reconstructs transient temperature and flow with reasonable accuracy under the tested noise levels. Using either constraint alone yields nonunique or physically distorted histories, demonstrating that complementary surface and terminal information is essential for accurate reconstruction. Training shows staged transitions that illuminate multi‐constraint learning. Overall, this work provides a practical route to integrate heterogeneous geophysical constraints into retrospective reconstructions of mantle dynamics.