I joined Jeroen Tromp's and Frederik J. Simons' Research
Groups at
Princeton University as a postdoctoral research associate in
Feb, 2020. I
received Ph.D. in Earth Science and Engineering from King Abdullah
University of Science and Technology (KAUST),
where I am advised by Gerard
Schuster.
My research interests lie in: (1) the application of 2D/3D elastic FWI to the land and marine surface seismic and VSP
data, (2) the application of machine learning to seismic data processing and migration, and (3)
3D surface wave inversion and migration. I have spent time at Los Alamos National Laboratory for
internship. I visited TOTAL for two weeks.

I'm interested in source encoded FWI, application of elastic FWI to the challenging seismic data,
application of machine learning to seismic data processing and migration,
3D surface wave inversion, seismic forward modeling
in frequency domain, superresolution imaging with surface waves, and natural migration of surface waves.
Representative papers are highlighted.

3D Acoustic-Elastic Coupled Full Waveform Inversion of Marine VSP Data from Fenja Field, Norway
Zhaolun Liu, Jurgen Hoffmann, Frederik J. Simons and Jeroen Tromp
Project Page

We apply the state-of-the-art three-dimensional (3D) acoustic-elastic coupled seismic modeling, migration, and inversion
techniques to deviated Rig Source Vertical Seismic Profile (RSVSP) data from the Fenja Field in Norway, to advance our understanding
of subsurface structure.

Elastic Full Waveform Inversion of VSP Data from a Complex Anticline in Northern Iraq
Zhaolun Liu, Jurgen Hoffmann, Frederik J. Simons and Jeroen Tromp
Project Page IMAGE (SEG), 2021, (Oral Presentation)

We demonstrate an application of isotropic elastic Full-Waveform Inversion
(FWI) to a field data set of Vertical Seismic Profiles (VSP) from a
structurally complex narrow anticline in Northern Iraq. Both RTM and LSRTM results show that
the shear wave speed (Vs) image has a higher resolution than the
compressional speed (Vp) image for the target structure, owing to the
presence of interpretable P-to-S converted waves.
The elastic LSRTM has improved the amplitude balancing and image resolution and mitigated some migration
artifacts compared to elastic RTM.

The seismic data with a relatively high signal-to-noise ratio are chosen for training to get the
learned basis functions. Then we use all (or a subset) of the basis functions to attenuate the random or
coherent noise in
the seismic data.

Irregular topography can cause strong scattering and defocusing of propagating surface
waves. Thus it is important to consider such effects when inverting surface waves for the
shallow S-velocity structures. Here, we present a 3D surface-wave dispersion inversion
method that takes into account the topographic effects modeled by a 3D spectral element
solver.

We recast the multilayered sparse inversion problem as a multilayered neural network problem.
Unlike standard least squares migration (LSM) which finds the optimal reflectivity image,
neural network least squares migration (NNLSM) finds both the optimal reflectivity image and the
quasi-migration-Green's functions.

SEG Annual Meeting, 2018, (Oral Presentation) Geophyscis, 2019

We extend the 2D wave-equation dispersion inversion (WD) method to 3D wave-equation inversion of
surface waves for the shear-velocity distribution.

Semi-stationary Supervirtual Interferometry of Reflections and Diving Waves
Kai Lu, Zhaolun Liu, and Xiaodan Ge
CSIM Annual Report, 2018 Geophyscis, 2020

we extend the application of SVI to far-offset reflections and diving waves by defining semi-stationary
phases.
Semi-stationary phases mean that the phase difference between adjacent traces in the common pair gather
(CPG) are very small, so that stacking the semi-stationary traces with techniques of limiting the
stacking
zone and phase shift compensation also enhances the SNR.

We have developed a methodology for detecting the pres-
ence of near-surface heterogeneities by naturally migrating
backscattered surface waves in controlled-source data.
This natural migration method does not require knowledge of the near-surface phase-velocity
distribution because it uses the recorded data to approximate the Green’s functions for migration.