Zhaolun Liu

I am a PhD student at the center for subsurface imaging and fluid modeling (CSIM) of 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 TOTAL and Los Alamos National Laboratory for internship. I did my bachelor and master at the Ocean University of China.

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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.

Multilayer Sparse Least Squares Migration= Deep Convolutional Neural Network
Zhaolun Liu and Gerard Schuster
arXiv:1904.09321, 2019

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. These quasi-migration-Green's functions are also denoted as the convolutional filters in a convolutional neural network (CNN) and are similar to migration Green's functions. We show that the CNN filters and feature maps are directly related to the migration Green's functions and reflectivity distributions. Thus, we provide for the first time a physical interpretation of the filters and feature maps in deep CNN in terms of the operators for seismic imaging. The advantage of NNLSM over standard LSM is that its computational cost is significantly less and it can be used for denoising coherent and incoherent noise in migration images. Its disadvantage is that the NNLSM reflectivity image is only an approximation to the actual reflectivity distribution. However, the approximate reflectivity image can be used as a superresolution attribute image for high-resolution delineation of geologic bodies.

3D Wave-equation Dispersion Inversion for Data Recorded on Irregular Topography
Zhaolun Liu
CSIM Annual Report, 2019
SEG Annual Meeting, 2019

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.

Convolutional Sparse Coding for Noise Attenuation of Seismic Data
Zhaolun Liu, Kai Lu and Xiaodan Ge
SEG Maximizing Asset Value through Artificial Intelligence and Machine Learning Workshop, 2018

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.

Neural Network Least Squares Migration
Zhaolun Liu, and Gerard Schuster
First EAGE/SBGf Worskhop on Least Squares Migration, Rio de Janeiro, Brazil, 2018
EAGE Annual Meeting, 2019

Sparse least squares migration (SLSM) estimates the reflectivity distribution that honors a sparsity condition. This problem can be reformulated by finding both the sparse coefficients and basis functions from the data to predict the migration image. This is designated as neural network least squares migration (NLSM), which is a more general formulation of SLSM.

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

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
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

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

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