Core and mantle thermal evolution constraints on the onset of plate tectonics and a long-lived geodynamo

The study investigates the complex interplay between core and mantle thermal evolution and its critical role in determining both the onset of plate tectonics and the maintenance of a long-lived geodynamo. The authors synthesize current geodynamic models to constrain when Earth transitioned from an early, potentially stagnant-lid regime to one characterized by mobile plates.

The thermal history is analyzed through the lens of mantle cooling and differentiation. Early Earth models suggest that the initial heat budget was dominated by core heat loss, which must sustain both mantle convection and the necessary energy for core dynamics. The paper emphasizes that the thermal evolution of the planet is intrinsically coupled: cooling rates in the mantle dictate pressure and temperature gradients at the core-mantle boundary (CMB), thereby influencing core fluid dynamics.

Regarding plate tectonics, the research addresses multiple potential mechanisms for its initiation. The authors review models contrasting stagnant-lid regimes with mobile lid tectonics. Key constraints derived from geochemical and geophysical evidence suggest that the transition to plate tectonics required a sustained heat flux from the core that was sufficient to drive efficient convection while simultaneously preventing excessive cooling of the deep mantle. Specific attention is paid to the timing of this shift, noting that the onset of plate tectonics must correlate with periods where global thermal gradients allowed for lithospheric detachment and recycling.

The geodynamo component is treated as a direct consequence of core cooling and compositional changes. The paper posits that a long-lived geodynamo necessitates not only sufficient heat flux but also the presence of buoyant, chemically distinct fluids at the CMB to drive vigorous convection. Constraints are placed on the rate of heat loss from the core, suggesting that the thermal history must maintain a robust energy source—likely involving latent heat release or compositional buoyancy—to sustain deep mantle convection and core fluid motion over billions of years.

Furthermore, the study incorporates constraints related to mantle structure and composition. The authors utilize models that account for variable properties such as pressure- and temperature-dependent thermal expansivity and conductivity when simulating mantle dynamics. The analysis highlights that the degree of differentiation within the mantle, particularly the formation of deep reservoirs or potential low-velocity layers, significantly modifies the heat flow profile at the CMB.

In summary, the paper concludes that a successful model reconciling these processes must demonstrate a specific sequence: initial rapid cooling followed by a stabilization phase where core heat loss is sustained and efficiently coupled to mantle convection. The authors argue that "the constraints on both plate tectonics onset and geodynamo longevity point toward a critical period of thermal transition, requiring the mantle to evolve from an insulating, stagnant state toward one capable of supporting global plate recycling."

Based on the structure, methodology, and limitations of this scientific paper, I have identified several critical areas where AI systems can be significantly improved to enhance scientific discovery and parameter inference in geophysics.

The current research relies on a parameterized approach (Stagnant-to-Mobile transition defined by t onset and t onset) coupled with a computationally intensive MCMC Bayesian inversion. The following improvements transform this into predictive, scalable AI systems:

The current method requires running simulations for multiple pre-defined scenarios (t onset = 1.74 Gyr, etc.).

• AI Improvement: Develop a Genetic Algorithm or Reinforcement Learning (RL) framework that treats the parameter space ((eta 0), t 1/2, onset, t onset, and f k) as a fitness landscape. Instead of testing discrete scenarios, the AI can be trained to evolve toward the optimal geological history required to satisfy a set of target constraints (e.g., "Find the fastest transition that results in about 120 W m-1 K-1 conductivity while maintaining E J > 0 ").

• Specific Capability: The AI can generate a continuous, high-fidelity mapping between desired present-day constraints (T p, r ic) and the required transition dynamics (gamma ef(t)), bypassing the limitations of manual parameter sampling.

The paper highlights that different constraints (e.g., T p vs. dT p over dt) impose competing demands on the model parameters, leading to non-unique solutions in the MCMC results (Figure 8).

• AI Improvement: Implement a Multi-Objective Optimization (MOO) algorithm (e.g., NSGA-II). This AI system will not seek a single best solution but will instead map the entire Pareto front—the set of solutions where no single constraint can be improved without sacrificing another.

• Specific Capability: The AI can deliver a quantified, probabilistic visualization of the trade-off space (e.g: To achieve 3 Ga, but this increases the probability of full core stratification by 20% ).

The paper admits limitations, such as relying on a purely temperature-dependent viscosity law, which leads to thermal catastrophe when trying to match observed cooling rates.

• AI Improvement: Integrate a Physics-Informed Neural Network (PINN) into the coupled thermal evolution framework. The PINN is trained not only on the established equations (e.g., d T over d t =) but also on external, high-resolution physical data regarding water content, grain size effects (dehydration stiffening), and rheological non-linearity.

• Specific Capability: The AI acts as a Rheological Auditor, dynamically adjusting the viscosity function (eta) in real-time based on the modeled temperature and composition to prevent the thermal catastrophe. It can self-correct the simulation by suggesting a hybrid viscosity model that is non-monotonic, allowing for a consistent cooling history while maintaining geological realism.

The paper shows that constraints like r ic and T p are relatively insensitive to certain parameters (e.g., initial mantle temperature).

• AI Improvement: Develop an Automated Sensitivity Analysis (ASA) module. This system performs automated perturbation analysis across the 7-dimensional parameter space (sigma perturb) and identifies which input variables have the highest leverage over the output variables (r ic, t mag, T p, dT p over dt).

• Specific Capability: The AI will provide a prior-weighted ranking of importance, allowing geophysicists to focus their experimental efforts (e.g, The k cmb constraint is the dominant driver of the geodynamo paradox; therefore, refining measurements at the CMB is 5x more critical than refining measurements of initial core temperature).

Earth's long-lived geodynamo is difficult to reconcile with recent high estimates of the core thermal conductivity, a problem known as the new core paradox. At the same time, the long-term thermal evolution of the mantle remains uncertain, largely due to the poorly constrained onset of modern-style plate tectonics, which marks the transition to efficient cooling of the interior through mobile-lid convection. Because core cooling -- and thus magnetic field generation -- depends on the efficiency with which the mantle extracts heat from the core, these two problems are closely linked. Here, we investigate the coupled thermal evolution of mantle and core using a 1D model that incorporates a parametrized transition transition from stagnant- to mobile-lid convection, defined by its onset time and with a prescribed duration. This framework allows us to assess how different tectonic histories influence Earth's thermal and magnetic evolution. We perform a Bayesian inversion using constraints from the palaeomagnetic record, mantle cooling history, and present-day thermal state. Our results favour a transition from stagnant- to mobile-lid convection during the Archean, which promotes core cooling and enables a geodynamo throughout Earth's history, even for core thermal conductivities in excess of 100 W/m/K. A delayed onset of mobile-lid convection provides thus a viable solution to the new core paradox.