I am an Assistant Professor in Geotechnical Engineering in the School of Engineering, Physics and Mathematics at Northumbria University. I obtained my PhD degree ("Outstanding Doctoral Thesis Award", top 5%) at Queensland University of Technology, Australia. I worked as a Research Scientist at the GFZ Helmholtz Centre for Geosciences, Germany, and as a Research Fellow at the University of Canterbury, New Zealand, before joining Northumbria University. 

My research focuses on characterising the influence of near-surface Earth structure on seismic wave propagation, advancing its observation, physical understanding, and predictive modelling using statistical, numerical, and machine learning methods. Notably, I have contributed to major national seismic hazard initiatives which led to the change of design practices, including the New Zealand National Seismic Hazard Model (NSHM), which underpins national seismic loading standards. In addition, I led the development of OpenAmp, the first labelled amplification database to unlock the potential of machine learning, now widely used globally, and contributed to the first national site database for China. One of my papers, “How well can we predict earthquake site response so far?”, is among the top 10 most cited articles published in the Earthquake Engineering Research Institute’s flagship journal, Earthquake Spectra, on a rolling three-year basis. Another of my co-authored papers, “Methods to account for shallow site effects in hybrid broadband ground-motion simulations”, received the 2025 Outstanding Paper Award, selected by the Editorial Board and peer reviewers as the most outstanding among 156 papers published that year in Earthquake Spectra. 

Funding

• The Royal Society Research Exchanges, “Advancing Site Effect Prediction with Ambient Vibrations and Machine Learning”, £10k, PI, 2026-2028.
• Seismological Society of America (SSA) Global Travel Grant, $3k (USD), 2026.
• NU: Opportunities Scheme, Northumbria University, £1k, PI, 2024.

From 2000 to 2019, earthquakes claimed 721,318 lives, which account for 58% of death tolls caused by natural hazards (UN Office for Disaster Risk Reduction). If we can reliably forecast strong ground shaking during future catastrophic earthquakes, we can save lives. Despite the potential benefits, accurate prediction of earthquake ground shaking remains elusive. One of the major roadblocks is our inability to accurately predict the profound modification (e.g., amplification) of the Earth's near-surface structure (i.e., the top few hundred meters of the crust) to the seismic waves that travel through it - known as “site effects”.

I specialize in the characterization of earthquake site effects. My research interests include:

• Site-effects characterization via observation, classic statistical modelling,  physics-based simulations, and advanced data-driven methods,
• Inversion of near- and sub-surface properties (e.g., using microtremor or seismicity data),
• Uncertainty/variability quantification,
• Site classification,
• Signal processing, and
• Database development.

For detailed information on my research, please visit my personal website: www.chuanbinzhu.com 

I am particularly interested in applying disruptive techniques, e.g., deep learning, in science discovery and problem-solving in engineering seismology.

Please feel encouraged to contact me if you are interested in research or knowledge exchange collaborations, or in pursuing a research degree under my supervision.