Seeing the Northern Lights at the equator? Predicting the next reversal of Earth's magnetic field

The geomagnetic field is a fundamental part of planet Earth, existing for at least 3.5 billion years. Yet far from being a constant, the field is very dynamic, exhibiting slow fluctuations and wandering magnetic poles which occasionally fully reverse in polarity. During a global magnetic field reversal, the usually strong dipolar field of the Earth fragments into multiple magnetic poles each of which drift across the planet's surface.

It is likely that around every one a local auroral oval forms allowing the 'northern lights' to appear anywhere a pole was close by - even at the equator.

The magnetic field wraps around our planet as an invisible force-field, providing long-term protection to life on the surface and modern technological infrastructure from harmful solar radiation. Although the current rate of reversals is about 2-3 times per million years, the last global reversal took place some 780,000-years ago, meaning that planet Earth is now well "overdue" a reversal.

True or not, it is certainly the case that change is afoot: a patch of weak field in the south Atlantic (the south Atlantic anomaly) is spreading; the global field (expressed by the dipole strength) is weakening at a rate of 5% per century; and very recent measurements over the last few decades have shown that the magnetic North Pole has begun a sprint away from its historical position over northern Canada towards Siberia. Does this indicate that a period of significant change for the global field is upon us?

Predicting the magnetic field is challenging, not the least because we do not yet have a complete representation that describes how the magnetic field changes. Although the basic physics is understood, the complexities and extreme conditions within the Earth's core make it computationally difficult to model. The novel aspect of this project is to apply recent advances in Machine Learning or Deep Learning to the prediction of Earth's magnetic field.

The key idea behind the project is that a trained neural network may be able to spot patterns in the data that have so far either not been noticed or have been too complex to interpret; such networks may be able to supply accurate short-time forecasts of the internally generated magnetic field. One of the goals of the project is to assess the evidence for whether the geomagnetic field is likely to reverse and how fast it might do so.