The Earth’s magnetic field, generated by turbulent convection in the liquid outer core, has reversed many times over its 3.5 billion-year history, at a present rate of about 2-3 times per million years (Stern, 2002) The last global reversal took place 780,000 years ago, leading to speculation that we are “overdue”. This fact, coupled with the observations that the field is weakening in the south Atlantic (the so-called south Atlantic anomaly) and the dipole is presently decaying at a rate of 5% per century, suggests that the magnetic field may be headed for a reversal. However, predicting future magnetic field variations is challenging, in part because we don’t yet have a complete physical description of the geodynamo within the core.
Despite these challenges, at our disposal is a large set of observations of the Earth’s magnetic field, describing its polarity state over millions of years, and in more recent centuries higher resolution observations from shipping records and ground-based observatories which have enabled maps of the field to be reconstructed (see figure 1). Since the beginning of the satellite era, several decades of very high quality satellite data now show the evolution of the geomagnetic field in unprecedented detail (e.g. Finlay et al., 2016). To date, studies using data to constrain the geodynamo process within the core have been reliant on a mix of human subjectivity and physics-based models.
The novel aspect of this project is to apply recent advances in machine learning to the prediction of Earth’s magnetic field. Machine learning is a technique in which computers ‘learn’ to interpret data via an explicit training process, using neural networks for example. Such algorithms have been used with great success in, for example, spotting patterns in consumer spending, speech recognition and in recommending movies within Netflix (e.g. see the Wikipedia page). In some studies, machine learning has been shown to predict the next frames of video from a static image (Vondrick et al. 2016). In this project, we will train neural networks to learn how the magnetic field has changed, and to assess its predictability.
The objectives of the PhD project are as follows:
1. Assess predictability for the million-year evolution of the geomagnetic dipole.
2. Investigate predictability of the global magnetic field using a 400-yr observation-derived model (gufm1, Jackson et al. 2000) using a technique that predicts the next frames of a global movie of the evolving magnetic field (e.g. Vondrick et al. 2016).
3. Assessment of predictability of a neural network trained not on real geomagnetic data, but on the output of a numerical simulation of Earth’s magnetic field: for example, the coupled Earth model of Aubert et al., 2013 or the database of simulated magnetic reversals under development at Leeds.
Year 1: Familiarisation with neural networks, geomagnetic observations and the theory of the dynamics of Earth’s core. Assessment of the dipole time series PADM2M.
Year 2: Application of machine learning to global geomagnetic data sets and assessment of predictability.
Year 3: Application of machine learning algorithms applied to the output of numerical simulations of the Earth’s core; comparison to the real geomagnetic signal.
The student will learn techniques of machine learning using both Matlab and Python, and will have the opportunity to take relevant specific undergraduate or masters level courses. The student will also have access to a broad spectrum of training workshops at Leeds that include techniques in numerical modelling, through to managing your degree and preparing for your viva. The student will be a part of the deep Earth research group, a vibrant part of the Institute of Geophysics and Tectonics, comprising staff members, postdocs and PhD students. The deep Earth group has a strong portfolio of international collaborators which the student will benefit from.
Although the project will be based at Leeds, there are project partners in both Edinburgh and Copenhagen who the student will visit. There will also be opportunities to attend international conferences (UK, Europe, US and elsewhere), and other possible collaborative visits within Europe.
This project is available for funding through the Panorama NERC DTP, please see View Website for funding details and eligibility requirements.
Aubert, J., Finlay, C. C., & Fournier, A. (2013). Bottom-up control of geomagnetic secular variation by the Earth’s inner core. Nature, 502(7470), 219–223. http://doi.org/10.1038/nature12574
Cox, G A, and Brown, W J. 2013. Rapid dynamics of the Earth's core Astronomy and Geophysics, 54(5)5.32-5.37
Buffett, B., & Davis, W. (2018). A Probabilistic Assessment of the Next Geomagnetic Reversal. Geophysical Research Letters, 45(4), 1845–1850. http://doi.org/10.1002/2018GL077061
Finlay, C. C., Olsen, N., Kotsiaros, S., Gillet, N., & Tøffner-Clausen, L. (2016). Recent geomagnetic secular variation from Swarm and ground observatories as estimated in the CHAOS-6 geomagnetic field model. Earth Planets and Space, 68(1), 1–18. http://doi.org/10.1186/s40623-016-0486-1
Jackson, A., Jonkers, A., & Walker, M. (2000). Four centuries of geomagnetic secular variation from historical records. Philosophical Transactions of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 358(1768), 957–990.
Mandea, M., & Olsen, N. (2006). A new approach to directly determine the secular variation from magnetic satellite observations. Geophysical Research Letters, 33(15), L15306. http://doi.org/10.1029/2006GL026616
Stern, D. A (2002) Millennium of Geomagnetism, online material: http://www.phy6.org/earthmag/mill_1.htm
Vondrick, C., Pirsiavash, H. & Torralba, A. (2016) Generating Videos with Scene Dynamics, NIPS https://arxiv.org/abs/1609.02612
Ziegler, L. B., Constable, C. G., Johnson, C. L., & Tauxe, L. (2011). PADM2M: a penalized maximum likelihood model of the 0-2 Ma palaeomagnetic axial dipole moment. Geophysical Journal International, 184(3), 1069–1089. http://doi.org/10.1111/j.1365-246X.2010.04905.x
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FTE Category A staff submitted: 79.20
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