Researches

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All Seismic attenuation models Seismic $Q$ compensation Seismic signal processing High-performance computing Deep learning

Seismic attenuation models

Frequency-dependent absorption and dispersion caused by the anelasticity of subsurface media can be empirically characterized either by experimentally established frequency power law or by physically based mechanical models over a wide range of frequencies. I try to figure out the connections among different attenuation models from both mathematical and physical viewpoints.

Seismic $Q$ compensation

Seismic attenuation compensation is an important method to enhance signal resolution and fidelity, which can be performed on either prestack or poststack data. I aim at developing the state-of-the-art algorithms and general framework that includes seismic inversion and imaging schemes to compensate the subsurface $Q$ filtering effects.

High-performance computing

The intensive computation and enormous storage requirements of wave equation-based processing, such as RTM and FWI, prevent these methods from being extended into practical application, especially for large-scale 2D or 3D case. The emerging graphics processing unit (GPU) computing technology, built around a scalable array of multithreaded Streaming Multiprocessors (SMs), presents an opportunity for greatly accelerating seismic data processing by appropriately exploiting GPU’s architectural characteristics.

Seismic signal processing

If you step deep into applied mathematics, you will find many great algorithms, such as Compressed Sensing, Dictionary Learning, Operator Splitting, and Nonconvex Optimization, developed by brilliant applied mathematicians. I apply and improve these algorithms to process seismic data.

Deep learning

The task of training a deep learning algorithm to accurately identify a nonlinear map from a few and potentially very high-dimensional input and output data pairs seems at best naive. Coming to our rescue, for many cases pertaining to the modeling of physical systems, there exists a vast amount of prior knowledge such as the principled physical laws that govern the time-dependent dynamics of a system. Encoding such structured information into a learning algorithm results in amplifying the information content of the data that the algorithm sees, enabling it to quickly steer itself towards the right solution and generalize well even when only a few training examples are available.

Featured Publications

Geophysics

We develop a novel adaptive stabilization method for $Q$-compensated RTM ($Q$-RTM), which exhibits superior properties of time-variance and $Q$-dependence over conventional low-pass filtering based method. We derive the stabilization operator by first analytically derive k-space Green’s functions for constant-$Q$ wave equation with decoupled fractional Laplacians (DFLs) and its compensated equation.
Project DOI Citations: 22

Recent Publications

Project DOI Citations: 0
Project Project DOI Citations: 0
Project Project DOI Citations: 13
Project DOI Citations: 22
Project DOI Citations: 25

Recent & Upcoming Talks

Mar 5, 2019 10:00 AM — 11:00 AM Department of Geophysics, Stanford Unversity, USA
Jan 29, 2019 10:00 AM — 11:00 AM Department of Geophysics, Stanford Unversity, USA
Nov 30, 2018 10:00 AM — 11:00 AM Department of Geophysics, Stanford Unversity, USA
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Recent Posts

Here I list some online learning platforms.

Here I list some online learning platforms.

Here I list some tools and softwares that I used in my research.
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Professional Services

  • 2012 – 2013: National Undergraduate Training Programs for Innovation and Entrepreneurship
  • 2014 – present: Member of the Society of Exploration Geophysicists (SEG)
  • 2014 – present: Member of the European Association of Geoscientists & Engineers (EAGE)
  • 2016 – 2017: Teaching Assistant of Advanced Mathematics for undergraduate
  • 2017 – present: Peer-reviewer of scientific journals
    • Geophysics
    • Computers and Geosciences
    • Journal of Applied Geophysics
    • IEEE Geoscience and Remote Sensing Letters

Awards & Honors

  • 2019: Outstanding Graduate Student, China University of Petroleum, Beijing, China.
  • 2018: National Scholarship for Doctoral Students, Ministry of Education, China. (top 5%)
  • 2018: Geophysics Bright Spots Paper, Geophysics Editors.
  • 2018: Outstanding Contribution in Reviewing, Journal of Applied Geophysics Editors.
  • 2013: Outstanding Undergraduate Student, Yangtze University, Wuhan, China.
  • 2012: Second Prize in CUMCM, China Society for Industrial and Applied Mathematics, China.

Skills

R

90%

Statistics

100%

Photography

10%

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