Yan Zhan
Carnegie Postdoctoral Fellow

Research Interests

Geodynamics; Volcanology; Crustal Deformation; Numerical Modeling; Data Assimilation

Academics

University of Illinois at Urbana-Champaign, Department of Geology, Ph.D. Geology, 2020
Peking University, School of Earth and Space Sciences, M.S. Structural Geology, 2015
Peking University, School of Earth and Space Sciences, B.S. Geochemistry, 2012

Contact & Links

Overview

Understanding the dynamics of restless volcanoes helps people be anticipated before devastating volcanic eruptions. Yan Zhan establishes physical models to decipher the early warning from volcanoes such as surface motion, earthquakes, and gas emissions. Yan also develops data-model fusion techniques to make volcanic eruption forecasts possible. The data assimilation framework catalyzes fast iterates of models to describe volcanoes' behavior by integrating multiple observations from geology, geophysics, geodesy, and geochemistry.

As a postdoctoral fellow at Carnegie, Yan will work with Diana Roman and Hélène Le Mével on exploring the mechanism of precursory deformation, seismicity, and other behavior during volcanic unrest. Yan will develop numerical models based on the laws of poro-viscoelasticity, damage mechanics, and thermodynamics to evaluate:

  1. deformation and stress buildup due to magma or volatile dynamics,
  2. failure initiation due to stress accumulation,
  3. failure propagation due to progressive damage,
  4. earthquake triggering due to failure processes, and
  5. fracture healing after volcanic unrest, which affects the initial state for the next unrest cycle.

Yan is also interested in the dynamics within magma reservoirs. Traditionally, a magma reservoir is described by a homogenous body of liquid magma surrounded by solid host rocks. However, a magma reservoir is a heterogeneous crystal mush, a mixture of crystals, liquid, and gases. The magma's behavior varies depending on temperature, composition, crystallinity, bubble fraction, and strain rate, etc. Yan plans to develop and perform a series of bonded particle numerical simulations to test:

  1. How is a crystal mush pressurized due to heat, mass flux, or differentiation?
  2. How does a fraction of crystal and gas distribute and redistribute due to magma injection?
  3. How do host-rocks respond to a pressurized or depressurized magma reservoir?
  4. What does a heterogeneous crystal mush look like geophysically, by seismology, magnetotellurics, and gravity?

Yan finished his Ph.D. in 2020 from the University of Illinois - Urbana-Champaign. Yan has developed a high-performance computing volcano data assimilation framework based on the Ensemble Kalman Filter (EnKF). By combining sophisticated geodynamic models and near-real-time observations (e.g., InSAR, GNSS, and seismicity), Yan has quantified the probability of failure for several magma system (such as Laguna del Maule, Korovin, Kerinci, and Sierra Negra volcano) to explain the restless behavior and evaluate the likelihood of volcanic eruption. Yan also builds generic models to understand how uncertainties in volcanic systems' material properties affect failure forecasts. 

Yan procured funding through the NASA Earth and Space Science Fellowship program to support the last two years of his Ph.D. work. He also mentored several undergraduate students during their undergraduate research program and thesis projects.