A&G Highlights Meeting Programme - January 2026
16:00 Prof Mike Lockwood (President)
Welcome and Announcements
16:05 Dr Giulia Magnarini (Natural History Museum)
“Mega Landslides in the Solar System”
Mega Landslides in the Solar System
In the late 1960’s and early 1970’s, images from the Lunar Orbiter 3 and Mariner 9 showed for the first time the existence of spectacular landslides on the surface of the Moon and Mars, respectively. These landslides are called long-runout landslides and they were known on Earth since the late 19th century. Long-runout landslides are hypermobile landslides that are able to move at high velocity over sub-horizontal surfaces for tens of kilometres. Such hypermobility requires a dramatic reduction of friction, which origin remains debated.
In this talk I will start with a brief overview of the record of mega landslides across the Solar System. I will then focus on martian and terrestrial mega landslides, which, thanks to the latest high-resolution optical and spectral images available and field accessibility, respectively, are being studied to understand whether they are linked with climatic phases of deglaciation. I will conclude the talk discussing the only extra-terrestrial long-runout landslide ever explored by humans, located at the Apollo 17 landing site on the Moon.
Dr Giulia Magnarini (Natural History Museum)
Giulia is a postdoctoral researcher at the Natural History Museum in London. Her current research concerns secondary craters on Mars, the Moon, Mercury, and Europa. She also continues studying long runout landslides on Earth and Mars, the topic of her PhD at UCL, for which she combines remote sensing techniques, field work, and laboratory experiments.
Giulia was invited as one of only a handful of UK members of the NASA Apollo Next Generation Sample Analysis (ANGSA) program, studying a recently opened Apollo 17 sample that was collected from a lunar landslide deposit.
Giulia was awarded the 20205 RAS Early Career Award for her innovative work on the mechanisms involved in long runout landslides across the Solar System. Giulia will soon move to the Laboratory of Planetology and Geoscience in Nantes, France, as a Marie Curie Fellow, where she will study ancient mega landslides in Iceland.
16:35 Dr Stephen Thorp (IoA and Kavli Institute for Cosmology, Cambridge)
" Pop-cosmos: Machine-learning the galaxy population for large cosmological surveys "
Projects such as the Vera C. Rubin Observatory and Euclid are critical tools for understanding cosmological questions like the nature of dark energy. By observing huge numbers of galaxies, they enable us to map the large scale structure of the Universe. However, this is only possible if we are able to accurately model our photometric observations of the galaxies. In this talk I will present the "pop-cosmos" framework, a generative model for the galaxy population, which we have been developing to tackle this challenge. I will show how we can write down a flexible physics-based recipe for rapidly synthesizing large samples of mock galaxies, how we can "train" this model to match our observations, and what we can do with such a model once it is trained. Along the way, I will try to highlight some of the key techniques and technologies that make this work possible: neural networks that emulate computationally costly physical models; generative machine learning tools such as "diffusion" models; and graphics processing unit (GPU)-based computing. TBD
Dr Stephen Thorp (IoA and Kavli Institute for Cosmology, Cambridge)
Stephen Thorp is a postdoctoral researcher at the Institute of Astronomy and Kavli Institute for Cosmology, Cambridge, where he works with Prof. Hiranya Peiris. He spent the first three years of his postdoctoral position in the Oskar Klein Centre at Stockholm University, and relocated to Cambridge in September 2025. From 2018-2022 he was a PhD student at the Institute of Astronomy, Cambridge, working with Prof. Kaisey Mandel. Before that he studied physics at the University of Birmingham. Stephen has broad interests in statistics and machine learning applied to astronomy problems, particularly in the fields of galaxy evolution (which he'll be talking about at the RAS), strongly-lensed transients, and supernova cosmology.
17:00 Dr Francesco Scotti di Uccio (University of Naples Federico II)
“From background seismicity to seismic crises: What can we learn from Machine Learning-Enhanced Catalogs?”
Microseismicity continuously occurs along the seismogenic structures that can also host larger, destructive earthquakes. Therefore, its characterization can potentially provide crucial information on the geometry and mechanical state of the underlying faults, before the occurrence of notable events. However, conventional catalogs are limited in size, since many small events are hidden in the noise. Therefore, discovering such events calls for advances in both earthquake detection techniques and monitoring infrastructures.
We showcase how the integration of machine learning and similarity-based detection techniques can increase the content of seismic catalogs both for background seismicity and seismic sequences, up to one order of magnitude as compared to conventional manual catalogs. In this framework, machine learning models provide an enhanced set of events as compared to the standard catalog, which can be used as templates to effectively detect lower magnitude, colocated events, also with a short interevent time. The location of the earthquakes in the enhanced catalog revealed the activated fault patches, while the determination of source properties enabled the definition of evolutive models for the seismic sequences. To monitor background seismicity, we integrated the standard seismic network with 200 stations deployed in dense arrays for one year, demonstrating the possibility to consistently detect small magnitude earthquakes with the use of short-term dense arrays and established machine learning models. Moreover, this configuration enables the downscaling of the seismicity characteristics to small, decametric-size events, achieving resolution as from multiple years of conventional monitoring.
We finally focused on the characterization of the 2025 Santorini seismic sequence, revealing the intense level of seismic activity during the crisis and demonstrating how deep-learning approaches can support near–real-time tracking of the evolution and dynamics of the sequence.
Dr Francesco Scotti di Uccio (University of Naples Federico II)
I am currently a PostDoc Research at the Department of Physics of the University of Naples Federico II, where I obtained my Bachelor’s and Master’s degrees in Physics, followed by a PhD in Structural, Geotechnical, and Seismic Risk Engineering (awarded on February 21, 2025), with a thesis entitled “Detection and characterization of microseismicity using advanced techniques” (supervisors: Prof. Gaetano Festa and Prof. Matteo Picozzi). During my PhD program, I carried out research periods as a Visiting Student at Stanford University and at the GFZ German Research Centre for Geosciences in Potsdam, where I had the opportunity to develop and apply advanced techniques for microseismicity detection, also using fiber-optic sensors for seismic monitoring.
My scientific activity focuses on the characterization of small-magnitude earthquakes through the implementation of artificial intelligence techniques for the automatic identification of earthquakes within continuous seismic signals and the generation of high-density seismic catalogs. These catalogs enable accurate estimation of event hypocenters, assessment of the energy released during the rupture process, and analysis of the geometrical properties of seismogenic sources. The results of this research have led to the development of an automatic strategy for the comprehensive characterization of seismicity in the area affected by the destructive 1980 Irpinia earthquake (Southern Italy), which is currently monitored by a dense seismic network (Irpinia Near Fault Observatory), providing continuous support for monitoring activities. Moreover, I am actively involved in national research groups focused on the development of advanced strategies for the automatic identification of seismicity using machine learning models, as well as on the use of fiber-optic sensors for continuous monitoring.
17:25 Dr Zahra Zali (GFZ Helmoltz Centre for Geosciences)
“Revealing Hidden Information in Volcanoes and Faults with Machine Learning”
Seismology is a strongly data-driven science, and the rapid growth of seismic observations offers new opportunities to learn more from Earth signals. Modern machine-learning methods enable faster and deeper analysis of large and complex datasets than is often possible with classical approaches. In this presentation, I introduce a deep-learning approach for the automatic analysis of continuous seismic data, applicable across different data types and tectonic settings. Using the 2021 Iceland eruption as a volcanic case study, I show how this approach can identify key eruptive phases and detect weak precursory tremor signals. I then present examples from active faults, including the East Anatolian Fault Zone and the San Andreas Fault, where the same approach reveals subtle patterns that are difficult to detect with traditional techniques. Together, these examples show how such analyses provide new insight into transient processes and stress evolution in the solid Earth.
Dr Zahra Zali (GFZ Helmoltz Centre for Geosciences)
Dr. Zahra Zali is a postdoctoral researcher in seismology at GFZ Helmholtz Centre for Geosciences. Her research focuses on the analysis of continuous seismic and strain data to study transient processes in faults and volcanoes, with an emphasis on developing machine-learning–based methods. She has worked on volcanic eruptions, slow deformation, and earthquake-related signals across a range of tectonic settings. Her current research aims to detect and characterize the preparatory processes preceding earthquakes and volcanic eruptions.
17:50 Prof Mike Lockwood (President)
Closing Remarks
18:00 Drinks reception - Geological Society, Lower Library
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