Zhaolun Liu

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 the application of machine learning to seismic data processing and migration and 3D surface wave inversion and migration. I have spent time at Los Alamos National Laboratory for internship. I visited TOTAL for two weeks. I did my bachelor and master at the Ocean University of China.

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Research

I'm interested in the application of machine learning to seismic data processing and migration, 3D surface wave inversion based on finite-difference method and SPECFEM3D, seismic forward modeling in frequency domain, superresolution imaging with surface waves, and natural migration of surface waves. Representative papers are highlighted.

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.

Convolutional Sparse Coding for Noise Attenuation of Seismic Data
Zhaolun Liu, Kai Lu
SEG Maximizing Asset Value through Artificial Intelligence and Machine Learning Workshop, 2018,(Oral Presentation)
Geophysics, 2021

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.

3D Wave-equation Dispersion Inversion for Data Recorded on Irregular Topography
Zhaolun Liu
CSIM Annual Report, 2019
SEG Annual Meeting, 2019, (Oral Presentation)
SEG Post Convention Workshop, 2020, (Invited Talk)
Geophyscis, 2020

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.

Deep convolutional neural network and sparse least-squares migration
Zhaolun Liu, Yuqing Chen and Gerard Schuster
First EAGE/SBGf Worskhop on Least Squares Migration, Rio de Janeiro, Brazil, 2018; EAGE Annual Meeting, 2019; SEG Annual Meeting, 2019 (Oral Presentation)
Geophysics, 2020

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.

Multiscale and Layer-Stripping Wave-Equation Dispersion Inversion of Rayleigh Waves
Zhaolun Liu and Lianjie Huang

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

The multiscale and layer-stripping method can alleviate the local minimum problem of wave-equation dispersion inversion of Rayleigh waves.

3D Wave-equation Dispersion Inversion of Rayleigh Waves
Zhaolun Liu, Jing Li, Sherif Hanafy, and Gerard Schuster

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.

Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test
Zhaolun Liu, Abdullah AlTheyab, Sherif Hanafy, and Gerard Schuster

SEG Annual Meeting, 2016, (Oral Presentation)
Geophyscis, 2017
SEG_PPT, bibtex

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.

Superresolution near-field imaging with surface waves
Lei Fu, Zhaolun Liu and Gerard Schuster
Geophys. J. Int, 2017

We present the theory for near-field superresolution imaging with surface waves and time reverse mirrors (TRMs).

An optimized implicit finite-difference scheme for the two-dimensional Helmholtz equation
Zhaolun Liu, Peng Song, Jinshan Li and Xiaobo Zhang
Geophys. J. Int, 2015
PPT

We have developed an implicit finite-difference scheme for the 2-D Helmholtz equation.


Thank Jon Barron for the website template.