'Putting the whole Earth into a computer’: Chinese scientists use supercomputers to solve one of Yellowstone’s most enduring volcanic mysteries — and now want to create a digital twin of our planet to predict its future
The goal is to simulate entire planetary systems at high resolution
- Tectonic forces likely formed magma pathways before molten material rose upward
- Supercomputers enabled a full-scale reconstruction of Yellowstone’s hidden structure
- Digital models now test competing geological theories against observed data
Yellowstone National Park in the United States has long been one of the most debated volcanic systems due to its immense scale and limited direct observation.
Scientists have struggled to explain how its underground magma pathways formed and evolved, but a Chinese research team led by Liu Lijun and Cao Zebin, using high-performance computing, has now offered a new explanation grounded in large-scale simulation.
The study suggests that tectonic forces fractured the lithosphere before magma moved upward through those existing pathways - this means the cracks in the rock came first, and then the magma followed, indicating that stresses from the magma itself are not responsible for the initial fractures.
Article continues belowA computational approach to geological uncertainty
For decades, the explanation for the volcanism has been that as the magma upwelled, it created its own conduit from below by brute force.
The researchers built a 3D model using Chinese supercomputers that reaches from the surface all the way down to deep mantle layers, combining decades of seismic readings, rock measurements, and electromagnetic data into one unified computer system.
The result shows Yellowstone's internal structure much more clearly than any earlier conceptual model.
Researchers can now test many different scenarios against real-world observations to see which explanation better fits the data.
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The study also draws attention to how computational infrastructure now shapes scientific conclusions in important ways.
Running such a detailed model required access to advanced supercomputers that can handle very large datasets, and the researchers involved in the study indicated this level of simulation required resources not always available in other countries.
This introduces a structural factor into scientific discovery that cannot be ignored, as access to computing power may now determine which theories researchers can fully test and explore.
From volcano modelling to digital Earth systems
Beyond Yellowstone, this research points toward a much larger goal of simulating entire planetary systems at high resolution.
The idea of building a digital twin of Earth means combining geological, atmospheric, and environmental processes into a single computational framework.
Such systems could allow scientists to test long-term scenarios and better understand how large-scale processes interact.
LLM-based frameworks may eventually help interpret the outputs from these complex simulations - however, their role would remain limited to analysis rather than performing the physical modeling work.
Despite the model's impressive detail, the findings still require independent validation from other research teams.
The study suggests similar mechanisms may apply to other volcanic systems around the world, however, this remains subject to ongoing scrutiny and further testing over time.
One researcher noted, "we are effectively putting the whole Earth into a computer," capturing both the ambition and the uncertainty of this goal.
However, relying heavily on simulation raises real questions about reproducibility and open access to data.
While the findings offer a clear and structured explanation, they also show how scientific progress may depend more on computing power than on direct observation.
Via SCMP
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Efosa has been writing about technology for over 7 years, initially driven by curiosity but now fueled by a strong passion for the field. He holds both a Master's and a PhD in sciences, which provided him with a solid foundation in analytical thinking.
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