This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The mantle, a rocky layer accounting for more than 80% of Earth's volume, circulates at rates of only a few centimeters per year. This circulation drives plate tectonics and influences major geological phenomena such as earthquakes and volcanic activity.
Despite its fundamental importance, the history of mantle circulation remains poorly understood because direct observations of the deep Earth are extremely limited. Currently, scientists rely on geological records of past surface movements and geophysical imaging methods, including seismic observations, to infer the mantle's structure and behavior. However, reconstructing historical mantle flow remains a major challenge.
In a new study, a researcher from the University of Tsukuba has developed an AI model based on a physics-informed neural network. The model was trained not only to fit observational data but also to satisfy the physical equations governing heat transport and fluid flow in the mantle. To test the model, the researcher first generated computer simulations of two-dimensional mantle thermal convection.
The simulation results formed a reference solution against which the AI reconstruction was evaluated. The findings are published in the Journal of Geophysical Research: Machine Learning and Computation. The model was provided with synthetic observations representing near-surface mantle motion and a present-day snapshot of the mantle temperature distribution.
Although information about past temperatures and deep-mantle flow was not supplied directly, the model successfully reconstructed these hidden features with high accuracy. The results indicate that combining complementary types of geophysical information is essential for recovering realistic mantle convection histories. With further development and application to real geophysical data, this approach may provide a powerful tool for revealing how Earth's deep interior has evolved over time.
Atsushi Nakao, Physics‐Informed Machine Learning Framework to Retroactively Estimate Mantle Thermal Convection From Partial Geophysical Observations, Journal of Geophysical Research: Machine Learning and Computation (2026). DOI: 10.1029/2026jh001310 BA art history, MA material culture. Former museum editor, paramedic, and transplant coordinator.
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