In 2015, the Earth Observatory of Singapore (EOS) received an endowment of S$1 million from the Stephen Riady Group of Foundations, which was matched one-to-one by the Singapore government. The goal of the fund is to provide opportunities for EOS postgraduate students to explore the Earth’s dynamic processes, and ensure greater resilience and safety of people, cultures, and economies in the region.
Now in its fourth year, the Dr Stephen Riady Geoscience Scholars Fund has awarded a total of S$52,920 to postgraduates Mr Olivier Bernard, Mr Rishav Mallick, and Mr Shi Qibin.
The review panel was composed of four EOS faculty members - Assistant Professors Caroline Bouvet de la Maisonneuve, Wang Xianfeng, Wei Shengji, and Aron Meltzner, who had the challenging task of selecting the winners.
Congratulations to the three award winners!
/user/olivier-bernard;">Olivier Bernard (supervised by Assistant Professor Caroline Bouvet de la Maisonneuve)
How fast does magma rise? Constraints on magma ascent rate in the volcanic conduit from decompression-induced microlite crystallisation in natural andesite.
Magma ascent rate (MAR) is the main driver for eruption style which affects volcanic hazards. However, MARs are poorly known, leaving large uncertainties about the threat of a volcano. In order to quantify MARs, decompression experiments and laboratory analyses on rock samples from the Rabaul Volcanic System in Papua New Guinea will be conducted. The underlying principle of this approach is that crystals form in the magma during its ascent inside a conduit, therefore recording the history of the rate of magma ascent. This novel combined method will help constrain the relationship between eruption style and MARs, which will improve assessments of volcanic hazards.
/user/shi-qibin;">Shi Qibin (supervised by Assistant Professor Shengji Wei)
Deep learning earthquake rupture plane and sense of motion
Knowing the geometry and the sense of motion on the fault plane is key to earthquake physics and seismic hazard assessment. However, the current methodologies used to estimate these parameters do not capture Earth’s complexity. For example, they make assumptions on how fast the seismic waves travel within the Earth, which can lead to biases in the results. This project will use a new method involving neural networks (a series of algorithms modelled after how the human brain works) to resolve the geometry and the motion of the rupture plane of earthquakes. Neural networks can infer complex interactions between variables, from direct observations. The new method will then directly extract solutions from the raw data, without assumption. It will first be applied on earthquakes that have been resolved in the past to train and validate the neural network. The network will then be able to provide robust constraints on earthquake rupture plane to improve future seismic hazard assessments.
/user/mallick-rishav;">Rishav Mallick (supervised by Associate Professor Emma Hill in collaboration with Professor Roland Burgman from the University Of California)
Geodetic observations of topography building
Elastic rebound is a popular conceptual model used to describe how the crust responds to slip on faults during the earthquake cycle. An important implication is that there is no permanent strain in the crust. But this is challenged by the existence of mountains and basins. This project will investigate how the Earth’s crust deforms elastically and permanently, by using Interferometric Synthetic-Aperture Radar (InSAR) data over the Tabas fold belt in Iran where there is active growth of folds in response to earthquakes. The long observation time span (approximately 25 years) of precise displacement measurements will improve the understanding of how earthquake cycle processes contribute to the evolution of our landscape.