Imaging Magmatic Architecture using Strain Tomography

Volcanic unrest can give warning of impending eruptions, thus monitoring and appropriate emergency management saves lives. However, the ability to accurately forecast the future behaviour of individual volcanoes relies on interpreting changes in the underlying magmatic system. The conceptual understanding of magmatic systems has evolved rapidly and there is now ample geophysical and petrological evidence that a fluid-dominated ‘magma chamber’ is only one component of a much larger system with a heterogeneous distribution of melts, crystals and gases. The opportunity exists to use these advances to interpret monitoring signals, to improve forecasting skills and in turn contribute to the paradigm shift in understanding. In particular, satellite technology has revolutionised the coverage, resolution and frequency of deformation measurements and is increasingly used for volcano monitoring. Dense time-series of high-resolution images reveal complexity and diversity than was not apparent when only infrequent point measurements were available. The latest images are more compatible with the paradigm of extensive multiphase, magmatic systems, but even the most recent models still rely on spheroidal chamber geometries.

The aims of MAST are thus 1) to analyse and model volcano deformation independent of constraints on geometry or rheology and 2) to link the long-term evolution of the temperature and melt fraction to patterns of surface deformation. These aims capitalise on the rise of satellite data, and recent advances in machine learning, strain imaging and the modelling of multiphase systems. The outputs will provide a scientific basis for observatories to interpret signals observed during unrest and to forecast future activity. Most importantly, the outputs will be consistent with – and contribute to - the latest understanding of magmatic systems.

The first work package aims to detect and classify the spatio-temporal patterns of volcano deformation using the rapidly growing global archive of satellite imagery. Existing machine learning techniques for analysing large datasets were developed on small datasets of ~30,000 images. We have now scaled up these proof-of-concept studies to analyse millions of images and have developed new machine learning methods that do not rely on pre-conceived models. Alongside this, we are developing new methods for separating different signals within time-series data, including separating deformation signals from atmospheric noise using deep learning, and separating signals from magmatic and hydrothermal systems. Finally, we have developed a framework to systematically compare the deformation patterns between volcanoes, and have applied it regionally.

The second goal is to understand the relationship between the long-term development of magmatic plumbing systems and the short term deformation observed in our satellite archive. Over timescales of hundreds of thousands of years, a hot, weak zone develops that flows in response to short-term pressure changes. Using numerical modelling, we found that relatively cold magma systems exhibit cycles of uplift and subsidence, while comparatively hot plumbing systems experience solely uplift. These predictions fit well with observations from magmatic systems where geophysical methods have been used to measure subsurface temperature and satellites have observed active deformation.

The third goal is to develop new modelling methods that incorporate our latest understanding of magmatic systems and can be linked to satellite observations. In the laboratory, we have developed scaled models of an inflating magma chamber (golden syrup) within an elastic crust (gelatin), where we can measure both the surface deformation and the pressure within the chamber. Our initial results show that fracturing around the magma chamber affects the rate of surface uplift, a process that is not considered in existing numerical models. We are now developing the next generation of numerical models that combines solid particles with fluid dynamics, allowing us to understand the effect of crack formation and magma flow on surface deformation.

Finally, we are combining our expertise to respond to an earthquake swarm in Ethiopia, where a 50 km long magmatic dyke has formed between the volcanoes, Fentale and Dofen. The high-quality satellite data from the CosmoSkyMed satellite reveal the evolution of the magmatic intrusion and surface fracturing in unprecedented detail and will be a major focus for research in the second half of the project.

Our research goes beyond the state-of-the-art in a number of areas:
1) Using the latest machine learning technology to improve satellite data analysis, enabling us to 1) automatically detect deformation signals from a global dataset, 2) separate different signals based on statistical properties, and 3) build a global catalogue of signal characteristics.
2) Combining approaches from across different research fields and timescales to understand the role that the temperature and gas content of the magmatic system has on surface deformation.
3) Developing new laboratory and numerical models that allow us to understand the effect that the interplay between fracturing and fluid flow has on patterns of surface deformation.
4) Combing a broad range of expertise to respond to a volcanic crisis in real-time, focussing on recent events in Ethiopia.

Periodic Reporting for period 2 - MAST (Imaging Magmatic Architecture using Strain Tomography)

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